{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/timeseriesAI/tsai/blob/master/tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "created by Ignacio Oguiza - email: oguiza@timeseriesAI.co"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Purpose 😇"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this notebook is to introduce you to Rocket. \n",
    "\n",
    "ROCKET (RandOm Convolutional KErnel Transform) is a new Time Series Classification (TSC) method that has just been released (Oct 29th, 2019), and has achieved **state-of-the-art performance on the UCR univariate time series classification datasets, surpassing HIVE-COTE (the previous state of the art since 2017) in accuracy, with exceptional speed compared to other traditional DL methods.** \n",
    "\n",
    "To achieve these 2 things at once is **VERY IMPRESSIVE**. ROCKET is certainly a new TSC method you should try.\n",
    "\n",
    "Authors:\n",
    "Dempster, A., Petitjean, F., & Webb, G. I. (2019). ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. arXiv preprint arXiv:1910.13051.\n",
    "\n",
    "[paper](https://arxiv.org/pdf/1910.13051)\n",
    "\n",
    "There are 2 main limitations to the original ROCKET method though:\n",
    "- Released code doesn't handle multivariate data\n",
    "- It doesn't run on a GPU, so it's slow when used with a large datasets\n",
    "\n",
    "In this notebook you will learn: \n",
    "- a new ROCKET version we have developed in Pytorch, that handles both **univariate and multivariate** data, and uses **GPU**\n",
    "- you will see how you can integrate the ROCKET features with fastai or other classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries 📚"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # **************** UNCOMMENT AND RUN THIS CELL IF YOU NEED TO INSTALL/ UPGRADE TSAI ****************\n",
    "# stable = True # Set to True for latest pip version or False for main branch in GitHub\n",
    "# !pip install {\"tsai -U\" if stable else \"git+https://github.com/timeseriesAI/tsai.git\"} >> /dev/null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "os              : Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic\n",
      "python          : 3.7.15\n",
      "tsai            : 0.3.5\n",
      "fastai          : 2.7.10\n",
      "fastcore        : 1.5.27\n",
      "torch           : 1.12.1+cu113\n",
      "device          : 1 gpu (['Tesla T4'])\n",
      "cpu cores       : 1\n",
      "threads per cpu : 2\n",
      "RAM             : 12.68 GB\n",
      "GPU memory      : [14.75] GB\n"
     ]
    }
   ],
   "source": [
    "from tsai.all import *\n",
    "my_setup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to use ROCKET on GPU?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This method allows you to use ROCKET even with large and/or multivariate datasets on GPU in Pytorch. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1️⃣ Generate features\n",
    "\n",
    "First you prepare the input data and normalize it per sample. The input to ROCKET Pytorch is a 3d tensor of shape (samples, vars, len), preferrable on gpu."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The way to use ROCKET in Pytorch is the following:\n",
    "\n",
    "* Create a dataset as you would normally do in `tsai`. \n",
    "* Create a TSDataLoaders with the following kwargs: \n",
    "    * drop_last=False. In this way we get features for every input sample.\n",
    "    * shuffle_train=False\n",
    "    * batch_tfms=[TSStandardize(by_sample=True)] so that input is normalized by sample, as recommended by the authors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y, splits = get_UCR_data('HandMovementDirection', split_data=False)\n",
    "tfms  = [None, [Categorize()]]\n",
    "batch_tfms = [TSStandardize(by_sample=True)]\n",
    "dls = get_ts_dls(X, y, splits=splits, tfms=tfms, drop_last=False, shuffle_train=False, batch_tfms=batch_tfms, bs=10_000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "☣️☣️ You will be able to create a dls (TSDataLoaders) object with unusually large batch sizes. I've tested it with a large dataset and a batch size = 100_000 and it worked fine. This is because ROCKET is not a usual Deep Learning model. It just applies convolutions (kernels) one at a time to create the features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate a rocket model with the desired n_kernels (authors use 10_000) and kernel sizes (7, 9 and 11 in the original paper). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = build_ts_model(ROCKET, dls=dls) # n_kernels=10_000, kss=[7, 9, 11] set by default, but you can pass other values as kwargs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now generate rocket features for the entire train and valid datasets using the create_rocket_features convenience function `create_rocket_features`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we now transform the original data, creating 20k features per sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((160, 20000), (74, 20000))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, y_train = create_rocket_features(dls.train, model)\n",
    "X_valid, y_valid = create_rocket_features(dls.valid, model)\n",
    "X_train.shape, X_valid.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2️⃣ Apply a classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you build the 20k features per sample, you can use them to train any classifier of your choice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RidgeClassifierCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now you apply a classifier of your choice. \n",
    "With RidgeClassifierCV in particular, there's no need to normalize the calculated features before passing them to the classifier, as it does it internally (if normalize is set to True as recommended by the authors)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha: 1.00E+01  train: 1.00000  valid: 0.50000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeClassifierCV\n",
    "ridge = RidgeClassifierCV(alphas=np.logspace(-8, 8, 17), normalize=True)\n",
    "ridge.fit(X_train, y_train)\n",
    "print(f'alpha: {ridge.alpha_:.2E}  train: {ridge.score(X_train, y_train):.5f}  valid: {ridge.score(X_valid, y_valid):.5f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This result is amazing!! The previous state of the art (Inceptiontime) was .37837"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the case of other classifiers (like Logistic Regression), the authors recommend a per-feature normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0 eps: 1.00E-06  C: 1.00E-05  loss: 1.35134  train_acc: 0.81875  valid_acc: 0.40541\n",
      " 1 eps: 1.00E-06  C: 1.00E-04  loss: 1.15404  train_acc: 1.00000  valid_acc: 0.44595\n",
      " 2 eps: 1.00E-06  C: 1.00E-03  loss: 0.85360  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 3 eps: 1.00E-06  C: 1.00E-02  loss: 0.76183  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 4 eps: 1.00E-06  C: 1.00E-01  loss: 0.74625  train_acc: 1.00000  valid_acc: 0.50000\n",
      " 5 eps: 1.00E-06  C: 1.00E+00  loss: 0.74401  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 6 eps: 1.00E-06  C: 1.00E+01  loss: 0.74371  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 7 eps: 1.00E-06  C: 1.00E+02  loss: 0.74367  train_acc: 1.00000  valid_acc: 0.50000\n",
      " 8 eps: 1.00E-06  C: 1.00E+03  loss: 0.74367  train_acc: 1.00000  valid_acc: 0.51351\n",
      " 9 eps: 1.00E-06  C: 1.00E+04  loss: 0.74367  train_acc: 1.00000  valid_acc: 0.51351\n",
      "10 eps: 1.00E-06  C: 1.00E+05  loss: 0.74367  train_acc: 1.00000  valid_acc: 0.52703\n",
      "\n",
      "Best result:\n",
      "eps: 1.00E-06  C: 1.00E+05  train_loss: 0.74367  train_acc: 1.00000  valid_acc: 0.52703\n"
     ]
    }
   ],
   "source": [
    "eps = 1e-6\n",
    "Cs = np.logspace(-5, 5, 11)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "best_loss = np.inf\n",
    "for i, C in enumerate(Cs):\n",
    "    f_mean = X_train.mean(axis=0, keepdims=True)\n",
    "    f_std = X_train.std(axis=0, keepdims=True) + eps  # epsilon to avoid dividing by 0\n",
    "    X_train_tfm2 = (X_train - f_mean) / f_std\n",
    "    X_valid_tfm2 = (X_valid - f_mean) / f_std\n",
    "    classifier = LogisticRegression(penalty='l2', C=C, n_jobs=-1)\n",
    "    classifier.fit(X_train_tfm2, y_train)\n",
    "    probas = classifier.predict_proba(X_train_tfm2)\n",
    "    loss = nn.CrossEntropyLoss()(torch.tensor(probas), torch.tensor(y_train)).item()\n",
    "    train_score = classifier.score(X_train_tfm2, y_train)\n",
    "    val_score = classifier.score(X_valid_tfm2, y_valid)\n",
    "    if loss < best_loss:\n",
    "        best_eps = eps\n",
    "        best_C = C\n",
    "        best_loss = loss\n",
    "        best_train_score = train_score\n",
    "        best_val_score = val_score\n",
    "    print('{:2} eps: {:.2E}  C: {:.2E}  loss: {:.5f}  train_acc: {:.5f}  valid_acc: {:.5f}'.format(\n",
    "        i, eps, C, loss, train_score, val_score))\n",
    "print('\\nBest result:')\n",
    "print('eps: {:.2E}  C: {:.2E}  train_loss: {:.5f}  train_acc: {:.5f}  valid_acc: {:.5f}'.format(\n",
    "        best_eps, best_C, best_loss, best_train_score, best_val_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "☣️ Note: Epsilon has a large impact on the result. You can actually test several values to find the one that best fits your problem, but bear in mind you can only select C and epsilon based on train data!!! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### RandomSearch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to do this would be to perform a random search using several epsilon and C values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0  eps: 1.00E-03  C: 1.00E-04  loss: 1.15656  train_acc: 1.00000  valid_acc: 0.44595\n",
      " 1  eps: 1.00E-04  C: 1.00E+02  loss: 0.74367  train_acc: 1.00000  valid_acc: 0.50000\n",
      " 2  eps: 1.00E-06  C: 1.00E-04  loss: 1.15404  train_acc: 1.00000  valid_acc: 0.44595\n",
      " 3  eps: 1.00E-05  C: 1.00E+03  loss: 0.74367  train_acc: 1.00000  valid_acc: 0.51351\n",
      " 4  eps: 1.00E-08  C: 1.00E-02  loss: 0.76183  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 5  eps: 1.00E-05  C: 1.00E-05  loss: 1.35134  train_acc: 0.81875  valid_acc: 0.40541\n",
      " 6  eps: 1.00E-06  C: 1.00E+01  loss: 0.74371  train_acc: 1.00000  valid_acc: 0.48649\n",
      " 7  eps: 1.00E-08  C: 1.00E-01  loss: 0.74625  train_acc: 1.00000  valid_acc: 0.50000\n",
      " 8  eps: 1.00E-06  C: 1.00E+03  loss: 0.74367  train_acc: 1.00000  valid_acc: 0.51351\n",
      " 9  eps: 1.00E-05  C: 1.00E-01  loss: 0.74625  train_acc: 1.00000  valid_acc: 0.50000\n",
      "\n",
      "Best result:\n",
      "eps: 1.00E-05  C: 1.00E+03  train_loss: 0.74367  train_acc: 1.00000  valid_acc: 0.51351\n"
     ]
    }
   ],
   "source": [
    "n_tests = 10\n",
    "epss = np.logspace(-8, 0, 9)\n",
    "Cs = np.logspace(-5, 5, 11)\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "best_loss = np.inf\n",
    "for i in range(n_tests):\n",
    "    eps = random_choice(epss)\n",
    "    C = random_choice(Cs)\n",
    "    f_mean = X_train.mean(axis=0, keepdims=True)\n",
    "    f_std = X_train.std(axis=0, keepdims=True) + eps  # epsilon\n",
    "    X_train_tfm2 = (X_train - f_mean) / f_std\n",
    "    X_valid_tfm2 = (X_valid - f_mean) / f_std\n",
    "    classifier = LogisticRegression(penalty='l2', C=C, n_jobs=-1)\n",
    "    classifier.fit(X_train_tfm2, y_train)\n",
    "    probas = classifier.predict_proba(X_train_tfm2)\n",
    "    loss = nn.CrossEntropyLoss()(torch.tensor(probas), torch.tensor(y_train)).item()\n",
    "    train_score = classifier.score(X_train_tfm2, y_train)\n",
    "    val_score = classifier.score(X_valid_tfm2, y_valid)\n",
    "    if loss < best_loss:\n",
    "        best_eps = eps\n",
    "        best_C = C\n",
    "        best_loss = loss\n",
    "        best_train_score = train_score\n",
    "        best_val_score = val_score\n",
    "    print('{:2}  eps: {:.2E}  C: {:.2E}  loss: {:.5f}  train_acc: {:.5f}  valid_acc: {:.5f}'.format(\n",
    "        i, eps, C, loss, train_score, val_score))\n",
    "print('\\nBest result:')\n",
    "print('eps: {:.2E}  C: {:.2E}  train_loss: {:.5f}  train_acc: {:.5f}  valid_acc: {:.5f}'.format(\n",
    "        best_eps, best_C, best_loss, best_train_score, best_val_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to this, I have also run the code on the TSC UCR multivariate datasets (all the ones that don't contain nan values), and the results are also very good, beating the previous state-of-the-art in this category as well by a large margin. For example, ROCKET reduces InceptionTime errors by 26% on average."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fastai classifier head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = concat(X_train, X_valid)\n",
    "y = concat(y_train, y_valid)\n",
    "splits = get_predefined_splits(X_train, X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9eyIWU7jIVTesSMeSw1VeD8M3gsvHBsdiq2h+PBBc2EJELvHZPISneK1I8SiW39XZtd3YkBF7DfqIiOKF7wOEBYGaV0SquXGT5LeMsETU0OVOQ40fvZqDpOQUV/ZFRImppLlvTufjpOQU3LKatVz9QkqJvuHYrdEayY7cRiQlp7AOJLniWy+dwsK3y7wehmOmpiWeOlARUzXvf7+jEFc/l+b1MIjIJb4PEBLFomBWQs/wBJp6/Nnxtbl3BK9m1rm2v7rOIWzLcbeBwsHSVvzHM8dwqLTV8X0dKY+NbuctfSNeD4ESgOTiNuWGxiZxw4p0/CKsdnBOHbvB+8X6jDp84tFDrk1MuWHpkWoAiKmABpFfnazuwKq0c3hgt3/qBUezI68JNR1DXg+DKCH98c0S1yeCGSCkhNM3PIGcOncKZ49MTOHzTx1zZV9m3b4uCw+/VYouly76b1yZjj/sdOaCqKFrGCXNffO+X3q+HwBQ1tLvyH5j0WcXHcX45IVGNO0DY1iVds7Q7zLkQ9GELpdfcbTaw5HEn+DnNj8BV1bESjJ+avlMY6dGn04MEpE2Nw4z04GDWeh1GBGRlo2n612fCI75AGGsXDQmmqGxSRytaPN6GBF9f0M2bll9yuthaHJrWXJ/oPj7tEO7C/87+keN1TbcmdeEpw5UmNrXfzxzDDesSDf1O4ks2JQJsNaIhGW7yIiSZgbm44GXpTJ4rCG/GZ+cxsnqDq+HQQrFw2FmYmoaY5MsA0BE9sVsgNDsRSMDie76w84i/GBDLs51DHo9lHlKedOqa2h8Ev/+9FHLWZZ2i77fs6PQcEZbvPDtTbDCcbX3j2LB44dRHYedkb0QS/VN/fr29kp+Q89sd14njE5MYdrizE/HwJhmVrlfj1Ndg2PMxnHAUwcq8P1Xsr0ehm17CpqV1oV85mAFbl+Xjbz6CxkdoxNT+J/1/nuuYug0QTZd/VwaPvLgAa+HQURxIGYDhOEnvdy67jlLDH16HeuYqWmJX7x+BkVN/lh6VNc1U6tieMzcbNaO3Eb8x9P+XJJrRiy//0qb+9HYPYJnDlQ6sv3zvfFXA699YNTrIbjKyk3HwbI2dA6OY4OLdS+J/EZKiW++mIlHohThP1LWho8tPGi6McT0tMS//PEAHnqrxNL4PvXEEVzx+BFLv+uVKx4/gt9sL7D0u7EUaHfbqrRzSKuM7Uy5cx2DuHtrgeX3RyTBWmw9Q+Oz38tv6MXxKh89V7F8EUqWNHbH37U1EXnD8wDha6fqcKTM+lLU4Iz2zatPKVliaOZacXNWAx7b649OW009w0gpbsEvN+d7PRRbfv9GERq64692z9DYJPpH/dvZUEo5WxfFaZFqBRohpURlq7XsM6f/tDP1sdckIHhjbOY+IvjYnWeaMDRmbMk4xZevv5CBqxYfnf335FT0zK3wj5/V7LZEsmh/OQZGJ003uZoKfK63Zjc6MSzfSilqMfV4u5nusUBKiRNVHa4FQftGJtDeP4rzvSO+6XgcHEdLX2JN4hEREVnleYDwoT2l+NFruajtHMLljx5Cs0fZRcLCdNv9u4uxLr3WgdFQrAu/HL/i8cP4+MJDtrY5Mj6Ftn77F7mvnarDJx87POd7GzLr8MKxmWW9fu0+uiGzDl9ZegLZtcaXPsfaLaCbySyz+7Jwo/xKRh0e2lOqdkAUEwobe2fP02/kNeGDD+xHo8lJnVXHE6uEgF/FUvaclZEWNPZieNzYRMaZhh5MGAh2x5IduU24Y302dp1pVrK9aG+Xzy5KxZVPpuJzi4/G/GQ1kdNi5+hLRInG8wBh0JbsBvQMT2Bv4Xmvh+KJnqFx1zLMxienNevLtfWP4oYVJ9GuIBClQua5TtQHliu7TWWx39EJ6zced2/Nx+TUNL7z8ml8+slU22N5aE8pukOWxgAzNxJ+VxzIPIzHDNNYC2QCibes2k2xErhJKZo5X1e3m8vsLUzALrx+EkvJc1aH2j00jm+8kIHfbiuM+tjyln7894uZeNpkcyy/C3ZSdmvifXj8wjXTkXJ/Nqkj8pqVhBQiIjf5JkCompMFwJ1w+WOH8SmX6v78bFOeZhff17MaUNLcj83ZDa6MJZpbX87CF55JU1uA3MC5+VBpKz7y4AGUnre2HNbO6f/3bxTN+feegvM4Ut7m6E11bIQjyGt8n1C8GxqbRKdGkw5yn9VjTjBzsNhASYvg613ewgZKZBw7GRMRqVPS3Gd6RQo5I24DhFMxWONoLEoQrK5TTSZdakW7ku24aU+BmiUyRh0NPEdFTdYChKqZXfmUlJyC/Ab9unihSUpaGUvZtd1ISk5ResB2o/bTitRq3L4uKyEDWhlnO1HeYqJTt6JsNb8uTafElVdvvhP7V5aewIIYa9LhND98splzQ34RvIR59VS9twMxgOdlIooVN6xIx7/HQaPSeBC3AUInriZP13QhKTkFNR2Dhn9HSqm5FO++XcU4WNpqaDsnqzvwxWfTsDvf/0tBnRAjK+585UDgvXWsoh3bcqxlhO7InSl0f+pcl+nfnZZAdZs3GRnPHa7CyepOF/fon9vX763NwnXLTnq2/0RaPtPeP4oBj5oPBY+JfcMTqFU0eRRvii1M8DT1xH8nSOvn0wuf7cmpaTy2twxdzLYkcowQ4s+EENlCiEIhRKkQ4hFDv5dA52EiIlIr5gOEbgaOgllsp2uMZyXsyGvClU+kRlweuiW7AT/ZmGdoO8HurcVNJjKDXNTWP+qbrnVRxWCw8U0bGZR3bsjBH3YWz/u+G3WovvT8Ced3osNPl8g9Q+Oo8ihgGm7OzUPIGyFWat/5xZVPpuIrDrzHzWTZXrfsBP7z2TTT+3jtVB0+9MC+ea95Xn038mKwK7dXNmc1oCespqtTvGriFklqRTvWpdfiobfcaVak+sjEY52/HShpxbHK2Fvt4oAxAFdLKT8B4DIA1wohPuPxmIhi1pGyNiQlp6BvOLZKkRG5KWYDhLFSZPt0zUzmVXW78axDO6amJb75YgZOVLlbG+XTT6biF6+fmff9WFjekJScgn3FLUq3qfr9ebjM2YLfbt4r+fWj2zU4hpY+527Ab1iRji97HDANFWt1Wv3qfJ+3zVqs7n/hW6WYmJIIr8Zx06pTuGlVpoKRxb+K1n7cv7sYv9leMPu9pOQU/GZbgc5v6ZualprBqxtWpFvermrTgTfO1JSzJw+nzxdCCAyMGut07EfnfRQ0Vumnm/Jw5ys5Xg8jqvHJaUdLGskZwRuIdwT+8/+FNZFPrTp+DoD55mrATHAxXo+5fuLkvRgZ41mAcHh8EknJKYYfn3G201ajCifPpmmV7UhKTvFFYfPOwTHkN/Tinh3RO/epFlrbUMXyhvAuwud7R/CWQ12u38yfm6FnJ2C2PbcRZ+qNNxQpaur1PJshNJBrdyh+DQBGc8XjR/DZRUc1f37IYDkALaqzf0rP9yHdxjLqtem1UR/jxGvJO5vEEHoccXqCwytjge704V3hd+dby/juH53AP9+/b/YGJhFNTk1jyaFK15buSynx88DkZixMaIa7frl35SQI+PCD+3H7uixH9yGEuEgIUQCgHcBhKeW8HQoh7hJC5Aohcjs62DwlFjCJeb6S5j6ctRC4c8uPXsvF13w0URevGrsZIPSaZwHCVo2sh0X7KyIWFv/e2iws3l8x+2+nDqxWNrsucKP9yNtlajaoY31G7WxWYqgj5TPBufBGJ2mV7YbrHPrJ6ZoufOTBA3O+d/OqTPzvlvzZzAXLTEQ9zAZI8ht6cO8bRfjF5vnZlJEcq2jHjSsz8HqWP7pGzxGrkT6H3KVTDsCLjObrl6fjtpAbk19uPuN6Mx8z+HYyzu6EQUFjr2clH7Q+Cx0DkSfQVh8/x6WEAV2DM4HG7TmNHo8kMjcyS/cWtWD50bN46kBF1Me+kddkqT4u4E6zLDf0uLBMzu5TFetL+ZYeqcKxOc395j4hmRbfg0ZJKaeklJcBeC+AK4UQH43wmDVSygVSygWXXHJJyPcdHVrcCX2+RsanUNRkfLLfsPg49DjihhXpuGaJf1baRNLlUkkRIi/5conxTatORfz+uQjNQVRd46nYztth2W2L9pdjVyCTQOX5YF2EzJ9IjVN6hsbx/VdyDNc59JNIQdDgUjo3r+uzarsxNGZ8+dE3XzR3A1XfNdNcwOvadCovImt82DAh2p9nNyDjh+u9vUUtuHur+aWNAxGWGk9NS6XNB65+Ng1rT9Yo2x7p+8YLGbj3jSKvhzGH1jK8xfsrHFtKGCkIdKyifc5kY7zxIiDwk425WLS/XMm2xqdmJjlHJ6KvGPndjkJ89+XTs/+2u9TTyMqH1r7Redd68Sg8K9aOnDrz3cT1DI1N4vnDVZicMr6qyM45fumRaty5wfvlzlLKXgDHAFwb7bFxEv92TaTn63dvFOLGlRm+WB1G6ni9YosoFvgmQKj6XGZme3n13fjOmtPRH2jSS8ft3RAXNPbiTMNMsXgrs92XP3Z43vciFVOfc7AMfF3fNexZd04/2Z3fjN9ut15PKprg6+rE+Sr8Zud0TZdmF+zQ3dsdS+Y5/WWv4W/lpp5hezs0sS8t23P9kbFjdYlb8DhhRaSA7tMHK3DF40fQM6zmJrGmcwh1Xc69zoko2julpLkPD+wuNlXKQyW36u6adeeGHKyOgSW8dk8JZi8ZzveOaHadj3ZOOFjaZvh6p7J1AJtNZMyfNfE+mjARMLLq1pdP41db8m1l6I5PTmPtyRpTAa5QtldRRNHSN+J6HWszlhyuwrLUakNL+d3q5utUQE4IcYkQ4q8DX/85gC8BiN8ZDh8JNpccGbeXjV/e0o/Ms9bLwZAzGESPP6pqqw+MTuDeNwoxaCJByM+uW3YSr52qM/U7ngQIpdReYqyKmYCaVsaiSkaHU3q+b/brb7yQgf82mZGmR0oZMWi4OsKF/e78Ztyy2vnnxYrQme0lh6uUbFPv9Ql2kHZCcL+T087f2HxnzWn8Zpu52pTBg2NZi/Xu2e39+p91O7VFVTlQ4t4yfLOZ0Fk1Xbj62TTNi9Tm3hGlxwkAOFw6UzOuZ0j9JMHmrIY5TRwidXj3o8mpaV+8V81wunTB8UAQwcjzEgx+V7cNYNjmDZcbtIJlRqlbVuncXUxomOlzi49a6jpvdlLjK0tP4P7dxYYf//tgJqxPsj5U1JJ9+WQNHk8px6bT9ZZ+/8l95rI1zTx1TT3D+Oyio1h6RM21lROCx49xFwLCPvAPAI4JIYoA5GCmBuFej8dEJly37CRuXetsncpwvcPj+MMbRbaDm0SxIq2yHZ945JCSYPzLJ2qwPbcJ6w3Uao8F5S39eGhPqanf8SRAWNs55PjBUu+S+khZGzYauDBTeTlqdJnq9cudK36qdZE4p/5TSKSiwsHAmB3/uzV/9uvlqdUejsS+4LO9JXsmgy1SJ2irot24zU0cjfzYrNqZpUEbMussj+PKJ1N1D9he3/bl1XfjWKV72RJ3rMs29fjHUspQ0zmkmUmjOtN315kmx5eJ785vxrVLT6C9fxRffyHD0X2pcv3ydHz4wf1eD8NXgolMLQYn/KSU+NLzJ5AeA9kUwbq+VvklKzkSFZkTfs++8CKeeMf6bEP16IITb0MWb96NvresvERt/TPLKZnx7Q9SyiIp5eVSyo9LKT8qpXzU6zGR/y09Uo1tuY3YluPD+ubkimOV7RETEuJVsJSFnRVVQV7fl/qBJwHCoXFvUzZ/9Fou/vhmieHHBy+ytC44TxroJKq6qOnOvCbUhtzEx/2bOeRKN+Ns5AvwRfvL5wW6tuc0zqt/46v7mrC7rJTiFk+GoPX+ifZc1XUO4Uev5kZdblV6/kIGops3lno3iYNjkyhp7nMlgzjUtE8yYbQ8uc+d1UsVrQNYbKARgV9UelwnlGLLEyazvNzk80OQq0Kv33qGx/Hgm8UYm7QWuFO9LHdkfAqXP3rIN018Riem0Kuo7IQKbr+Pg0vY/R4cp9jg5Ns3FursVbcN4MMP7Edjt7uTEV49MyPjU6hotb4ay6w7X8nBfz133LX9kf9NTk0bXobtmxqEVnlxDLRycWCnDsqtL8+vj3jPjkJcu9TfnZ6AmQsqt05ULx2vQXtYp8x7dxbhltWnkOHTjELUHRAAACAASURBVJXQd8U1S+wdyK3OFNl5eRa+XYoj5W2mOkmG78+r65ifbMzFDSvUZOzGS0dMt+0649+uy6GO+7gelyYLb0mrtc2M7Mrtz3lr36hnnZz97LyCJbLxqvR8PzadbsBbBf5oQlLbOYSe4Qk85ZOmOjesSMdlj84vU+O20NPt9LTEvW8UoqS5T/sXFInn5kbkHl4tztiW04jxqWlDJX6eOViBF9POKh6B9VeiwUKG9d1b83Ht0pMJWd//bPuAZg18Mqd/dAIdA9YaJ927swifeOSQobhMzAYI3bwfP983iqTkFEw6XBhai9aSlbGQuk9pFXNnmL3uuhV8fW5adQrPK6oTaGi/Gt8PXQLnp3m10PexmWLskZidKZrzGTLwpIyMTzl0w+3cK6J3nMirt5+GrprKIIreCcDK8fN3OwpRrlOLUnUAaHRiynIRf9X+Z725ZeGx5IvPHsNzhyoBADvy/Lss1qzPLErFIp0bejuZYlr2FvkjsBSuqKkXLwUas/Qqq43oMhcv+sweyuL9hi/41I/5sAZr+8AYtuc24YevOt9l2I/XDFpiIQg1PD6JjafrYyLbLd4VNfVGDLJLKbHDB+UyXjh2Dk8fqFS8Vevvu9YotdUjyQ0cP4I1m+u7hiKWH8tv6MFPN+ZhyqOYgxVSSqxKO6cZe7hmyQnTNfBj3eL9FUhKTsH0tESVwhVIn198FJ964oil3zWTlOHbAOH31p5GcZP1GUEV15LhN6d2Lo7MjCc3bEksoH+yr+scwvmwGlB+Ot9uCzu5TE5N42eb8nRf38Zu57Ic7DQaGBmfQpfC4KuTHfbMbNvI2+VfHzqALzxzzPqAtPats/Ph8UmsPVnjeNdGrwUzEPcozFxRXcD9jTyDs39i5nWz61/+eADfesmfjZL8INpNVU2HsTqSjd0jWHF0ZmY+ZoNHFmw63YC9hWpLOvxyc370BykyPD5puEP1jSszdIOlVjh9jdEfvgzGwA7NdDBUdebNONuJjy08hHQDpWbIvs8/dXS2C7bbVwXxfRXivsf2luOPb5YgLRYz9C3o9/FEwo0rMyKuqDl1rutCo6g44WTwfHxy2nCpiS88k4Y71mfPm2D6+etncKC0FW0WgpBeKWzqw1MHKvDb7YkVBNSzOjApu+ZkDb78/AkUKGrI2D/qTpk+3wYIM8524YE353a66xwcc77ldOAKYFtOA77wTFrEh+zMazJ8YW7FzSa7B9+zw70PZG3nEJYdqbY141ffPYz9Ja342sr0OWmyod1ujTZ1cYpWg49vvpiBKx63FrmPxEog+7OLUl1pvR5paMEC5kFpFpp76P3N4bNPi/ZV4PGUchwqc6/LsNP0Pjrh3aJnlug7PCDFDpa04tKHDqKoyf7J8EyDfzscT3iU3ehkIpXdbZc09+HLGp1wjbyN8+p7LC+dsELVR8uLz2gsPk9GDAfObYfK2kz/ruFJDIWCNY6zI0zsOkVlSYuuwbE5mSp+r5bR1GNt8rhWReOtWDsZ+1xPoDb7aIJ02jU6gZRd242k5BQl11B2uXGv4TYnP8XPHqrEHeuzIyb6RJJX34OPLTyEGhMloqSU2JBR6/l9cqhgQpVTY7r6uTRc9ughR7bttODnOMWnK0y0+CdAaOCipPR8P74SuPnQOk8Pj0+aKniqVY/nbZ3MglwLywz2G6ixoCf0oij8qbK97MHE0fK2tVl4/kgVOgeNF6pu6x/DXa/lRjxwLHzrQtttJ5dw2wlo1nUNz7Y690Nn55a+UeX1duwu8RifmsbnnzqKw2VtpmfnQvd8e1iH3+CM6+iEv5Y2rTxaja+FzLZGW9IfbSYwUuH3gdEJfOiB/XMavLjNyrsi+DkudqEmlJeeSHGuCcWrp+od27ae8KzjnLpufPGZY4YzQp89pL8EKNph5qZVmbhxpZq6oG5QEUyJdOyNhTBEpL89X0H3wImpWPjrzTtc1oZDpTPXgX6JwfWPTuCKx4/g8ZQyR/fjh1d0bHIaQ2OTGB6fxD6bzeD88vqRO0IP0S+fqEFScoql5Z9Gg0CpFTOTI1oNGWNB8LMWiZQSRyvaMOV5wF39Jzm4cqPbZGPScxorPlr7RufVeD9a0Y6Fb5f5uhGaajUdQzG/wuXlk7Wu77O4qc9yQpt/AoQGNYcF9MIvUu9Yl41/f9r4MkjVs/Ba6/D1ancZsT7D/hvL0KE4ygHbas2mQ2VteOZg5WytqwtjcvcEseuMtQyDR/c6cwHtlwvN0FfBShp05+AYmnpG8NCeEtN3zaFvuToVs/zRdmLo4RKl5/vw6SePRDzRP3uoak4A7L5dxfMeE6onJAAYqXbJ9twL78vH95ahY2AMPUOxcTIcD9x4xSut4O4ZBcEQvwn/6D65rxx1XcMob9GfGGm0mNUTSUuff5fVDI5N4pebz5i++DfKL+eDoIrWfmwwce2RXWsvi+7UuS7lpRG06J0R7n2jCINjk5iYmsYH7kvB61naAXujp5Yfv5aLuzbmmRukw4LLuA+Vms/WjEXjk9P445ul+PnrZ1DU1IuqtgG0Dxg/3ngdzkgEaT7p2A1EvpR95uDMPczktL8mrb0gpcSLaWcjfob+7eGDmg2NDpS04gcbcvFKRp3DI4x91y47ge+GNSodDmTcmimpQYnpQKn1ybCYCxBGYyW7b47ACcFqZkBBY6/mOnwzKcRG/fHNEtvbCB7cOwbGMDg2NwB4+7qs2a+TklPmZA4eq2jXvCEoilBfcENmHVYcPevJ0rxgVkK6Rjfj8MyZaPX7dp1pwq+2uFdvKpyKTpRaNzYjExdOPl0mb4Rb+kbRESVbLvy5NRIkHh6fstTxVu911PvZPdsLcf3ydLT1j+Fktbv1cdam1+KB3cV4I6xhhNvBdKNuWZ2Jf3v4oNfDcIxW7Vmzk98/3JCDRXE647suvTbqcijVheg/8uB+jE5MoX90Ai197nTm3ZrdgL1FLVh5VHU3RXVUHieuXXoSC992NrssqOx8P7778unZG3Cv1XYMYXB0EtMSeGD3hessrWfXL8Hd6WkZs52qh8YmsTW7QfNYMa+Gm4VjSnPvzAqjwbFJfPn5E7hq8VHDv+t5wlMCWJZa7fUQXOOH5izT0xLfW3s6+gMjKGnux9MHKvHrrQVzvl96fub+T6veu53aeiMOLUdPSk7Bkz68Pov1rDkv+OBjFRd8HSC08yJ7VUcluLxuOEJGzdWBLrO3r8tSVsNw42n7S9HquoZxtKINn3riyLxMxZM6hbfv3JATsYlAhkYQLkjrdZVzvp77ILsv57jibpW/3V6Itwu9qyegshCsEJFfE6vLqZdHuXkeMJhtVtTUO9u0w8wsf6jgLO8xk7USd+WbD0aalXm2Ezs1amZNTUvkKypouzu/CT/ZmGviN8wdeAsDkwGJVqQ/NIP04wsP4sE39bNIUyva8dKJGqeHZYud2mZKanzpqGobmFPXZ2xyGvuKW3Dt8yfw2UXGb/LNOFndgdM1Hi3zsnmRa6RJVbTSCGZFGnJOXTe250Tughm6BC1SmYVo2w7n9/p5QU7ev6w4ehafW3wUDV3GS+2EcuMp7Bkaj9iwYeFbpUjeVYzTNZEnnj++UH0NKjNL2vWC7zHy1iMfUFVDtLl3BFctPjpvZR1g/BjTPzpheSlz8Pp6KCxod/1y58qEXKnRvXV6WhoOuGo9bI3Pr8/CNfeM4Fdb8jE64X0NTz/E5Jxs+pmIbAcIhRDvE0IcE0KUCSFKhRB3qxiYUVofdKtvlGBGXKQDuJGDz24DgQW9oJtXdlrIztIS6WQVymiHJy8ZzcKYmpZYeqTK4dFEVtMxaKso+1DIzZmb2Wn/G5Z5GfqxGgk50T1/2Nrzejzk/RXszmqmHl5lhMCo1ZstPbeuzTLVYEhAWOqC95tthTjowBKy8MNheLfyRNI/OolNpxtc2ZeTs6OhZ71os/yZUSaCVAo2Pwlv4PXb7YU47+CS5NvXZeM7a+ZnV0hIJCWnYNE+NZ2B156smW12Ec7JoNfqtHOaP1PRiRwAbll9CvfunN8Fc0t2Ay59SH3m8eTU9GzBdDuMnhP9cGMUKv3szPkvUikLq1TfeF3+2GFc8dj85YfBgPXIhLqSFaGfn/M2M42ZmeK9qWlpqs68HY3dF94vzb0j+PLzx10rgWDE9pxGNPeOaE7AANrBSNUruaanJe7bVYQyh2tmayUYfOD+ffjRq2YmwmNnQklLQWMv3i4876t76hh/SimEigzCSQD3SCkvBfAZAL8QQlyqYLuadp1pUvbBNhLQM2Nz1sxNou2lzi5LKbJXtNkMrRPTsyFLi2LlQuyf79+HpUfMLYlYl16rW9PIqOuWncTvbHSwDjb+cKzun0HZtd22bujCA/ddGlkxUkpDNbK+snRuF9bK1gH8xzPadU2jBcQjuWHFSdO/87WV6REzKGo0ihs7zYllnT1D40jeWeSLGVEr2vtHcbZdfSkJYCZgE7rc3YlDZOh59dNPpuo+9q2wDGqt5URBZo7p9+0qnpNlf8MKfzUuCf4tpxRlFz6eUo5bQoKf9QYmJMw0CtOi95I4EbwLtdziUsLqtgE8f7gqcnMXCSx44ggWaGSZOMnNm02z+/JjeYpImXtm6x9nnO3CfbvmB5+13L/bfEme0Pq6wbdcLAYWRiemcLZdfZO90YkpvJ5VP+/z2Ng9jMf2lmHaZvPBzVkNWH38wkTGksOV+Penj6GpR12Q8EBJC6ra9M/br52qi/qYoOFx7QYdflDXOYQPPbAfu/PVdX0/3zeCLdmN+PFr5oJ0KqVW+Kd+ZVBTz/DsPa8flpTHmi8/fxy3rM409Nhg/eqK1oGoKxL8aGdeE+q7vL0fD2U7QCilbJFSngl8PQCgHMB77G5Xz2+3F2JSUae7LI2AQaTzv6q08HCfjDCT6ndWOngFZddFDp6GBmujbd1sACE8BV7PvuKWOU0jVHtsb9mcmkZWadVGM+vm1ac8Dcg+/FZpxLoz0ZYFt/WPQkqJHQZfq42n6/Gtl07hYKCT5IjB91BTlAYMbxVEXmq+v7hF84K8pFndLOvjDjXQAfSbK2l1XbPj2UOV2JrTiJ0Wmwm5oW94QrOQ+pVPpuKaJcdNbW90YgoDBjJD732jaF6Xb9XsnOGSozTqMWNLtvlszPTqztnyFvFwIV4TZeLGzuSQaTrPZ+iP9J72Tz+pJmj37TWnsSy1WjOTpHd4Yk7dpvDLNiP1MgH3MnUdFUORrPquIfSYrLd1oLTVVPkQK89GaH3dWD6q/GpLPq5ZcmJODbf8hh4kJafYqgn3L388gAd2l2Bfceuc7//89TNYl16LMpsNGu/fXYzF+y9kaqcHlsOqbDD5001nlGxHSok9Bc249KGDsxMsTjW0siNYPmh/cSsGHW4wF4uBGi1ayS159T3YUzA/2ejFtLP4/FPHlPQKSFRVbYPI0YgZhAveN6QUt+AbL2Q4OSxTwo+NwEzG7csnauZMJNyzoxA3rnRn3Eaub5TWIBRCJAG4HEBWhJ/dJYTIFUIYnl7QWxb4psnMvyWHqyIGlSLdSBytaHO1kYYfTyDRfGaRfnaJntB06H3Freix8PePTZh7fcwUPv/562ouFozwywWn2ZuhpOSUORmfdlVHmZkNH9/Z9gF8+slUrEuvRWPITLJeYCCYadessONqJMEZ85+9fgbXLDkR5dHanJqQ0GPlfaBScN7Bz/GdH2/MxfdfyVG2vf9+MRMfM1Bby6nMRFWMTBo5+bLeti4L31s7c+kRujTM70I/52auOwZGo9/YjU9O4zGdCQQ7n7OnIxz/u4fGsPJodcTjcFu/mhv6CYsTY3sKmlHU1IvH9pbhxpUZUYNF+0vmX9RH5OeDVQzoG57Aov3lSjJiQ0kp9SeSE+xlO31uJrA2EdJ999XMOgBA5rno5SLWp9fqlrQJlj+57NFD+NZLp0wlEcypP27ydalsHdAN+E9NS2S5VEf2QEkr7g5r2KGqYVDf8ASuWnwUJRHui+28lava9LNK7X5MVAZyQ9V1Drm+0uRBjcSOm1ZlznvdAeDpAzPnyGOBCWUvrumd1tA1jFU6pUqc2ueHHtine01cp6gsVPrZTkey+vaXtOKJfeWz75Egu52pt2Q34H/Wq0kkUBYgFEK8C8BOAL+WUs6bMpJSrpFSLpBSLlCxv9DZ9a88b+wmvMVgraIfbMhF5jmPCpMH6N14D/k4dd0KI41Wfv+G8WUkerQCUVaWiVoRD5ktQSuPedfJM7gEL/xzmnmuK2rShNMzmuHLx186fg43r5rfzMcNO0zWBVR1UrUq+Nr5+VNiZJm6GXYzLFQKv4C9cLjy8ysy33SMHmcjjTra0m09u/ObsC69NvoDo4lwUI1U2uDlk7V49lAVTlZ3zmZqh1JZK6ncZK2ru7cWmJqdD13WCAAfW3gQP389b8739hQ0zzbl8rJAemP3MA6X6deZHRybxNn2QQyNTeLHr+XOKRERel1i9CZ2fHIam7MabC8hfTylDC8dr8H+YrVlbtal1+Jf/ngA7SEBiuHxSfQMWb8BCz5PsXR4UXXN+ejeMvxuRyHKzvfPaRYVrnd4wvA5UkW85CtLT+h+rlcfP4dvrzntSs3cbgevLTPPdaK5dwQrQ5oABp+/5anVyhtOeW1PQbPmMu3xyWl88dm0efXMnXZUY+XIXNY+b27dG2ae68S3Vp+aLes0OTU923XaiMbu4Tl13u9Yn4WnDlQ4FgiO5O2i85iYktjlwkqj7NpufOGZNEOPXfhWqeHjTHAFW//oBKSUEWvfW3HfruI5r48dSgKEQoh3YCY4+LqUcpelbVi8uNqW04DKKDMgWpKSUyx3avWS1Y5TbnjBw6CREVpZqW7dUIYXOPbLfJJbAVItZi4UH9tbhh9qFCM2cpJafvSsrSXy0YRnQizaX2F6GYeq0ZkNrO8806Q8k8OM2bdBLN2B6YhUiN/Pwj+HwZpgRhuwxNrLVtDYg+KmyOeEpw6oaUBil9bstZFj5qSDxzk9j7xdip9szJsXSAhdLWH33PftCM1jImnqGYmaJWPEwOjkvKVCuxQ2d7PjS88fx49fy9VtYnXb2ixcs+Q4UopbcLisDc8dqrKV0bLyaDXu312Mt4sil9gwKnhNNKX44PF2oEZqS8i1TVXb4Oz9wq1rLyx0yq3rjljTTqtxUCxQEXw7UNI6r6bzV5efnNcsShUppak6lEauW4NZRkab9nxl6Qm0D8x9rNUanuEZl04mkC143P26q066e2sBHt5TGvFnwe7Jfmz4GeRktqDZuvGhweN7thciu657duJkyeEqXL88HRWtxibcvvhs2pwMtWD5Lrfuof1anzzzbCc2ZNbNOa8Y9XpWw7za936goouxALAOQLmUconV7ViNnmvVLLHS7TOeBaPUTjOzlFePEw0QUsvVd3K1Q9XSAy1OnZ8i3bBGe2s9sa/c1j5LQ7JFQlOwj1a0oz/k37/eNj/NPyi3/sLFvpmTWbTn0ali1HZfvrwYa5QExEYGoRldUconmC3Kr2e7gi7SWu85FcvypcS8Gy+vbTrdgK+ttN8ARVUTiN9u1z5++VWk42Nj4P3il+uwOxyu3Qm43KQk7N/BhmORmlgFBY81Z0LOC0avCRvCusYWNPbOHtv6DSx119MaWNnzSkadre3YcfPqU/j8U/Mbkd0SFggLJhTE4UrBefpGJvDTTXm4c4N2OQ3VpZh2mgy4X7X4qNL9B0VaxhvOSFLL73YUJsR7RY+dM6PKTuxu2JLdgCPlzjdKOWkyG7agQfs6szCwPN9oBmB4ckXw7R38tp33e3jWfiSXPRr5HFfT4W0JHjvv1VKbnb+/tiLd8EpaM1RkEF4F4HYAVwshCgL/fdXsRl46UaNgKBfoFddPRB9feAgeJRNEVdc5hAWPz820GTbRVMQorawzwJsLPiOF0v3o1cz5S8LDbyBUO6IT3K0NWxardeGmsjFI0Jv5zVhxdH7WrJmU/Ugzd0cVdGO79WXzM1leO1Hl3xlhJxhpTrJof3nETMTwe/t7VZRhsHkg1LvQkZD47CLtGzo3l6hEcsvqTOwzuMxxQ6B2lxVpYcs/UkL26cQxSo8fu9vqKWzs1WxOosfq39mu855sNViyJtzQ2KShwMr31p7GwrciZ9DYsTVn/kSC2WyX107VmXq8XiBSq1GgX/k1g0W14BJEvSZtdrOGgp2sg5nOtZ3+rrPrN2ZW3nUOjkWcoLPzEqo6e7T0jbpWJxKYO+4t2Q3YqVNb04z7FDZqM8PO6zDbmd1kSkJScgpezaxTdu8spZzTjEjLqEb/gfDVebFEqyP7hx/cb6hETHFzn+WVtHoutrsBKWU6/LNSUld7/yje/7d/4fUwPOPWzcDmLHMdKHdFajjj8n1LSXM/pJSuFpF1OmDrVMJopKyQNTYD/Haedi/rOmplLF6/3HhW0rVLT0b8fqzPPFt5WYKBZkc7iArxPgCvAXg3Zo40a6SUy1TuY1XaOewviR5sajfQuOGl4zOfrUv+8k9tjysau285vTpIaVG6jX7qCW+XSOXU9UTtlrfJQL3cSKSUWHygAjd87B9xZ1iDGzs1BiPvS/tnmWc7MebC/izVpTPw5ns8Rb9ju+pzQcRlP4FdfPHZNEvbnNMVV2e8dkvJdA+Nz5Z8sVsnMFx5ywA++U9/rXSbsWJ0YgoHS/21GkWF9oFRvOtPL8b/eaft20LDghO/m07V48ZP/KOSbXYPjeMdFwn85Z+9I+LP8+p78E9/839cOZ8C3tYmDRVcgly3+Po531dRJsHuX7g+oxbrM2rnjS0oWiPPNJ3agMVNffjppjx88O/ehTV3XIE/vfii2Z8JzA/qTU1LXPQnkf8iJ1/J8pYBfPnf/t7BPeizcr/x8FulePdfzXyOgueynLoe1++l9ZzvHcHQ2CQ+9O6/9Hooc4Se+7WWygebzP3w8+93a1hzKO1i7HffXnMaR6IUctaT4UKRWyc9f7jalf2oWDYX7XLWidowVVE66aqm6l7mcFkb8hvcW06q18nOqnydFHivmX2dVN2KGe3UbeWG340u7T1D49h4ut5Sp+NtEbJcFJoEcI+U8lIAnwHwCyHEpSp38NSBChRp1LYL1RGlsLheAMFozZhQZ9v1ZxmdvKar97gBjh3BwKfVJj6T0xIvHa9RspzZsAiv5a1rs+YsGRwZN3gcMHkQDGYE/WDD3Kz90AkVszcQfq9vaeWjM+JgNtp9u4pml4Mtj5DlrsXI3+H0Cp3lqRfGu6dgJoBhJNvaDV7XazarsKkP1QayS658IhXffCHT1LZVXbdp1Wi2smzwk48dxuciZKoHjx83rcrE11a4eByOon1gFEnJKThgtGt6CL1DqNHjZYri5kBO0CrjE0x6eWyv9sTR73YUorl3BMerOjTrDYd69pCaMlnR/Pi1uefG549UWd6Wncs2u+fVSAHw4D3JHeuz8ZknU6NuI7euG/0jzpRq+tzio/iSguW3H3lwvyPHfr9MIEQStwFCrTd9fqP1QEq0WQwjnKitZ5SR9f1WeJHB9aPXtJcLW+Vk44pIVBV1/fFruVh6JHLwd2wyNpbDGO0wboRPJq5sO2VwyUW0OneRPPhmienfsWJ5qrVJCSc7+0opW6SUZwJfDwAoB/Ae7cc7NpSoDulMaN24MgONJpb2pxS14JolJ3DV4qOav6d1saIi+3x9hoJuuh5J3qlg+bZLZrsLG3jJdrrQBdCovCjZm0ZoBR39ehH+wjHj12RSytlln3qCf+vQmP65v7F7GL/dXmh4/044dS7yOS50kjk40WL1CKT6ekBrdYDRpjluO1zWZvgG2ewyNStBLT3hn4ernzs+/0EaJ+RH3i7FuUBAMbQMQaSX32y9MCfrY5YFynJszja38soovfszVZc2BY29mtdJUs4EgILNYqwIf59d+tBBjUfqS6vswKtRyoPodehWKVrXebcEr+2sHib1jq8nqjqiftZGJ6Zw8+pT+MGr2rVOnXK+dwQvHDtrKIYxNjmNw6Vqj3d+F7cBQi1e1+HTq8FEFxgJni1xaabHKU53fVp9/By+9ZI/L1oj2WuzG2LQw4rrNuktm4xVblycPHfY+oyoW4QQSQAuB5AV9v27hBC5QohcJzJXpJRYcrgq6kVztOYgvRpNuiIJZhw2947gf7fmR3xMvATXVVO9LNeu3uGJiDUbpZT46vLIJQv8LhaOF15atL8CH3xgPyamppUs38qq7UZ2oP6f05O8WnUGv/ty9OsTq9dJwWCXXg29eKY52RPh6bTyFB+rbNetxer0uST8M/BKRh1+rFNn3A47NWdDh6m6nITeUxxsPhFe3398ctqR1yZdY4WdhMTNq0/hmiUzwd6qtgFsNFmq47lDas4NK4+dnbk/0HnDl7cM4AvPHMM3XsiY9zO9mrR6rB5eD7oQkAqObU+Bvfsvq39j8Phupm67Kj/ZmIdnDlaixmRXaCOOGawjb3by/W6Na3cnxG2AUOsAuCrNmSw6UsvIwcbMsplEVaiwS6rTfrlZzYHPSNDkZ5vOGN5epkaWg5ZYyNpkHAgQQrwLwE4Av5ZSzklZlFKukVIukFIu+BMHrqb7RiawPLU66nJ9p27btSbKtP5Sv2ZgqRK8eaHICg0szfIzNyZ5Dus00lJh46mZm2pVBfXdZKTQuharN57BYvZOlESJSYFDeGg9SjuB5pKwY4LWlg6XteG2tfoN03xePUCZYPNFvWf90bfLZusO2xGcXAxfnWN2At3o58/oCqzrlp3EuQ51AZnnDlWa3l7wfBbp/T84Non6ruHZLGYpJVLL25TWcdVqqLEitRrdQ+Ozz/kjb+vX3A0aGJ1ErU6QKztCVuT/rM+e8+9tudbK+gSfQTcbnqma0AouXXdigkyv+ztg/dhrN5BrhnvVaF1W0BjbF7Sx5P337VO+TT92V/zsoui1FKJJDylGeryqQ0mBYCBxLrAisXJs1+uKbNdBxctunODW+8XrzrRa/MQsTAAAIABJREFUhBDvwExw8HUp5S6392/lPet00eeOgbG4ySA0+3fYWf5E/jfpwtKR8JqjQ2OTmJyaxsUXqZ2HT95VjL/5i3cq3aaf2b15U1m+JJYNjM7cDC/eX4Gnbv644/sLdrRfZqDMiJeN5lRSMZHmdCmOLdkN+MwH/sbRfegxU8pJq3lDqBU2EkUqDJSy2VvUgl9tyccfb1BXpvrbGiu7njtchaJm87GLe3bol4ooiFArtDpwzaPqk+fFRzj0Ou/XWyM3jHSL6Vr1DjxhL5koI2dk73GbQeh0EWVyVmO3/5aFqLjQ/GHIEghVwUHAv4GYRGS1kYGbgjPZiUjMRNrWASiXUi5xe/9mml05deMU6TbG6y7CqrSZrC/lhm+/dMrrITjmnIVGAm54PsqS5TvCMihUu2tjXtQbNz8yPUlg8Bhl9ZrcaDyhrX8MXXFYDkS11Apjk6PBJdpOl8Jxi1d/RejnSeX8W05dt+mlukFP7a9QOJK5whs5qHj7aCWMRNr25izjtRyTwzoYRxK8nohW7iXcep2s6Qad2tEj41PKJ2pDn795mw55DrXq3HYMjOHtwsiZa37pVlzRaq5+argTVR1ISk5xvQmV1qTCIQvLyxcp/lzHbYCQEpQ/jlWuS+SAuB+zTcnXrgJwO4CrhRAFgf++6tbOvQjmT01LQ/Vs4mEp8acNdM1z2xkb3T7TKjsMPe5QaasnkxMql4vpkZBRa3iFBtR35aubgAultTwsEjPLgW5ZnYn7d0e/YQUMNswz8VGeknI2s8fo5KzZ+8LrllmrjWkmQHX7OmeDvongvl1FSEpOwQ8DHci1Srb4rS4roBEEVHRKU7nEVIVbVp+yXBOvR6cMT36D/WZRXtp5pkmzK7abjDYZDKdVy9EpofdPD7xZgg8+sH/eY36wIQe/2pKP3mHt844f5xG+a6Jx1NacmcByQUMvBkYnsCXb2pJrFdoHRnHXxjzP9h/EACHFFx8epMhZGWetnYgpMUkp06WUQkr5cSnlZYH/1NdJ0DA6oS570+hN+rr0GlS1Xcj00vw9je8zCK/PyYvjrTnGLlTv2piH/3w2zbmBKGYl8SBaLUQrHd2ddrwqeoA3p64bOXU9prJfVGrrH8PVz6UBAH6x2Xh9XgCYmJIw2sjpSFkbkpJTzA7PML06XDQj2rHK6I3xymOxVQP8dzsK8V+B97gVbf1zg3E3r87E0iNVcwLfkT4H0Sbdgl11g92MvfbNFzN1g4TlLf2zSxn9GBTKq+/BRx8+OKecU6wZiXKNmK3R9MmK0NdQ6/xzPpBVp1eqw+5bIVgzViWjQdrQ1XwTU9P445slEesWn+0YhJQSfcMThpuQWKG6mZFVDBAShYiXWihE5E9GlrWodr7X2LJbrRuDSIfFui7/3oxX2lxuEi+CdcfigZHs1lcy6pwfiEnhxeAjuWV19CXoTgfp601mnwYzWx/dW4aPLTxk6He2ZFsLgE5PS2zw4WvrZ26v/JvwWYZdJCqznYua+rD0SPWc1Ttml/hJCRwLfI46B8fw9IH5v9/SN4Kk5BTk1LmX2ffNFzM1f/bV5SeVL2WMxsrKBjudp72mchLZacHjTCyXIngxpHntr7cVoHMw8kTjptMNWJ9Rh59sysWdG3J8UdKiTyMbeHpa2l6tFLdNSojMOtPQg19vc6+FOBH531QMX/iYZWZZxekadbPYqrm9PMMnZXjmWWzwRq41Bho5sM6uSRYOW5HqT2lpMlmTCzC3PDvU4fI2LIlSU1KFzzyZiqv/9e/w5Dc/5vi+/C5aFlO4/hFjWaTATADEzOPjjdb5IjRQEXTq3EwW1ObsmXqDQ+PGJn2cyqbWuhyq0Qi+OlmDUI/qRoR+CYClFLUo3Z7dv8rLax8pZwJhBU3WSriE/u37io3V/Ctq6p299p2Ysv7smfnN7RodppOSU/Duv/rTiD9bllqNZanV+O9PvsfC6GYwg5Ao4PGUsjnL8IiIvBR6TTo4Nv+Gyuo1q0/jWb52uMy5zudOMHNTtUPjAtRJZm8sWn3YfCbefGaRs/U7jXQlDSfETOF+o2psNM1p7R+dXWbH1STOuX1dFq60WCvWr6+K1rLA0ONcv4KMbiOfoSdSygxty+5bfG/RhRqrWt2XVWQ+a43TzTqYfslI3JFn/lytl33q9nFORYmJ4OdozYkarM+oxX/rZLxacV6nUUmk+p3FTX2o12k8o8fIddC9bxRp/iy89EHQscqZJdB2JlaZQUhEROQzUkqsOn4hoyBS84CvrUy3tO1pabw7KODfDDk3/fi1XN2fb8vxpn6cFjOz28+5kJ1FkSlfTmbjs6q1tMpLZu5fRyamcPVzx7Hy1stt7TOtst03EwJCiPcBeA3AuzETH1sjpVxmdXuRnk4VMQIz54jwgEVF6wAyz5kLHvvplDQxNY0PPzi/uUM0VhtSRKv5+fLJucG69oG5kyvB18pulp2Rkg6xtFzWDDOBteAj3QrGLU+txjaDtYv9GnDX0xkIek1OS1S1mSsn88jbpfjXv/8rfOtT79M8htTo1LKN9BpavQ6f2Z7lX3UcA4REAU4USSUiCmfkZiq3vsdYt1IDwrucdw2Zm1X080WMXzx7KDaDbMyUcp5e1kSbgczIihioqblof7nXQ5gVfrwz6/uv5CgaiRKTAO6RUp4RQvwlgDwhxGEppbE0sTChn/eJwJLvaEu/VWT95NZ1Y0HS32j+/NaXs2zvIxIr9evMmtB5/pzYu9Gan0G3r/Wuu/dNq6LXV41myqP6loNjk3g8xT/Htbz6ntnar3qMlmN46kAFuqJMChU19eouV7dy+ZBX34O6ziFc97G/N//LmBvU3J7bZPj39he3zAa1v/Wp91nat5Us+Ej8NMGhhUuMKa4crWif01WMiCgW6d10mJUV1vUuUjYiJSY/vxdeTIutTql+YrXen1UvHa9xdX+JQkrZIqU8E/h6AEA5gKiFpbRuQHuGJ2abOO0N1DNbe9L51+6xvZbimaRAZViWlYo5ocJG83Xfpi0G+vS65zpJ73MhLCyrMPo7Wq/PTavsL6UNHcOqtHNoiLI09uaQAK+qucSbVmXinh2Flo8Ju/Oboz8oTGvfKH72+hlL+9MS7ytrGCCkuJK8q9j27DERkZPu3lrg9RDIBCNdkbW6yfndxLQ3mfNGMnuePlDpwkgo3mw67a/l/qoIIZIAXA5gXrqdEOIuIUSuECK3o6MDQzp1G5ccnvu5UtndV0thU5/j+6jVWRpo11uF5zV/ZiZw0tRjrVaZyhVOKuoC9gybX92QUmy+wcahUmPNIyIptvme8yq53s3aimZE+nzZCZK1a9TPCzJTezYap+vrmqX31tKqK2hGtJfFyMoRBgiJiIhcdLwq+jIRI0sPyR3tBgo9u52xpcqLx+Z3zqQYFmcrxms7h0zfhPbFYYdcIcS7AOwE8Gsp5bxZcCnlGinlAinlgksuuQTr0iM3jQCAY5UdeGhPiYOjdU6wtmpdl34w8DfbCnSDem4Jf+9+/qljmLTR/TSWDRvswBzqro15lvdXct7ZoHRr3yi2ZBubjJBSKisZY0ezTgOOaL71kv3l4qFSK9oxrBME/Nnr1l/7WBHp3KYiUzRozMbEAmsQEhER+cxvthV6PYS4siO3EadruqM/0IKS5tjNWj9RHT1YTeSVzHNdOFXT5fUwPCWEeAdmgoOvSyl32d3e+OQ0XjtVb39gIZ4+UIk6BzP4goIrhKJl4e/Ob8bu/Gbc9Mn36j5uuwcd3M3U1KvvspZx6DS9AHSiMJOVdrSi3cGRxC69Zcbpiur9OcVKhqmUcs4ybzsZskZ0DLKLMREREVFEv3+jyOsh+FKHgexIih3Zdc4Ewb2UyH10xMzd5DoA5VLKJV6PR0+0hgEfX3gQ//x373JpNDOiZZ/eG+G80DEwhpqOQcP70H972itUtiy12tbvO0VVs4ZEMRKD3Zxvffm08ZURIR8CMxnfelmVTteetLv13pFxXHyRuc93+8AY3nnRhcW7/aPms2rdwgAhEREREbkm3gt8G1XjQtYVxbSrANwOoFgIEUybu19Kuc/DMVnSPzqJ/AbzzS2c8N01p3HF//3/Iv7sDzuLTW1rwmTNOKNLTacdiowbqWd4sroDV/3z3yrdbzwF+rNr3c1qHhh1r2xCQWMvLnvfXyPzXPS/MVhLOLyu5U825hraVywHmq9dar4h6h3rslHZNoCnb/64AyNSiwFCIiIi0uTUjQpRorvzlRyvh0A+JqVMh900NJrnVI26peu3rZvXM2bWNUuOz/vevTuNZbN7uYz39nXZSL7uX5D0//+FZ2NQod9GYE0vezOnrsfydq14aE+pa/v6xgsZqFt8vaHHanVBPljapnJIjnDqoJqUnKL5s9mO4jFwSc0mJURERKSpvCV6F18iIiInXLv0BDLP+TPbqPS8MzVovW5qUd81jJ9uiu1GEYNjzizhNNJoLkjF/Oru/Gb7G6GEoaJrPAOEREREpCl8+QgREZFbKloH8LCLWVREqnQOjmFvYYvXw8C+Yu/HQAEu5YRLjej0M4cqkZScggmdGpMMEBIRERERERGRcolcd/aAw91qjThS7s2y32mHm41YsfCtUpzrSNz6vy8drwEABgiJiIjIGiNFzYmIyHux2DGV4h9LGceO+3eba9Sj5wP3+6+f0obMOm8HEAOfBQYIiYiIiMg1PR7X1yKKV2fbB70egiOq4/Tv8qvDZe5mnLnZqTfRHKtsNxWg3ZzVYGr7DP6ac75vxOshRMUuxkRERETkmv5RZ4rHExGRfZ2DY67u7xOPHHJ1f4nkzldyHN3+20XnZ79+8dg529vrGeYEoteYQUhERERERERErlNdqq70vP1OrmRMxtmu2a+XpVbb3t4DCpc4+1FqebvXQwAAXPrQQc2fMYOQiIiIiIiI4kYC98XwneRd7gZ9Np1uwDsu4jsgFnUOxncGYXGzO8Hruq5hy7/LDEIiIiIiIiKKG3pdOin+TUyxOB6RFUoChEKI9UKIdiFEiYrtEREREREREVnxZsH56A8iIqI5VGUQbgBwraJtERERERERERERkUuUBAillCcAdKvYFhEREREREREREbnHtRqEQoi7hBC5Qohct/ZJRERERERERERE+lwLEEop10gpF0gpF7i1TyIiIiIiIiIiItLHLsZEREREREREREQJjAFCIiIiIiIiIiKiBKYkQCiE2ALgFICPCCGahBA/VLFdIiIiIiIiIiIictbFKjYipfyuiu0QERERERERERGRu7jEmIiIiIiIiIiIKIExQEhERERERERERJTAGCAkIiIiIiIiIiJKYAwQEhERERERERERJTAGCImIiIiIiIiIiBIYA4REREREREREREQJjAFCIiIiIiIiIiKiBMYAIRERERERERERUQJjgJCIiBKGEGK9EKJdCFHi9ViIiIiIiIj8ggFCIiJKJBsAXOv1IIiIiIiIiPyEAUIiIkoYUsoTALq9HgcREREREZGfMEBIREQUQghxlxAiVwiR6/VYiIgocbEsBhERuYkBQiIiohBSyjVSygVSygVej4WIiBLaBrAsBhERuYQBQiIiIiIiIp9hWQwiInITA4REREREREQxiGUxiIhIFQYIiYgoYQghtgA4BeAjQogmIcQPvR4TERGRVSyLQUREqlzs9QCIiIjcIqX8rtdjICIiIiIi8htmEBIRERERERERESUwBgiJiIiIiIh8hmUxiIjITVxiTERERERE5DMsi0FERG5iBiEREREREREREVECY4CQiIiIiIiIiIgogTFASERERERERERElMAYICQiIiIiIiIiIkpgDBASERERERERERElMAYIiYiIiIiIiIiIEhgDhERERERERERERAmMAUIiIiIiIiIiIqIExgAhERERERERERFRAlMSIBRCXCuEqBRCnBVCJKvYJhERERERERERETnPdoBQCHERgBcAXAfgUgDfFUJcane7RERERERERERE5DwVGYRXAjgrpayRUo4D2Arg6wq2S0RERERERERERA5TESB8D4DGkH83Bb5HREREREREREREPudakxIhxF1CiFwhRK5b+yQiIiIiIiIiIiJ9KgKEzQDeF/Lv9wa+N4eUco2UcoGUcoGCfRIREREREREREZECKgKEOQA+JIR4vxDinQC+A+AtBdslIiIiIiIiIiIih11sdwNSykkhxC8BHARwEYD1UspS2yMjIiIiIiIiIiIix9kOEAKAlHIfgH0qtkVERERERERERETuca1JCREREREREREREfkPA4REREREREREREQJjAFCIiIiIiIiIiKiBMYAIRERERERERERUQJjgJCIiIiIiIiIiCiBMUBIRERERERERESUwBggJCIiIiIiIiIiSmAMEBIRERERERERESUwBgiJiIiIiIiIiIgSGAOERERERERERERECYwBQiIiIiIiIiIiogTGACEREREREZHPCCGuFUJUCiHOCiGSvR4PERHFNwYIiYiIiIiIfEQIcRGAFwBcB+BSAN8VQlzq7aiIiCieMUBIRERERETkL1cCOCulrJFSjgPYCuDrHo+JiIjiGAOERERERERE/vIeAI0h/24KfI+IiMgRDBASERERERHFICHEXUKIXCFErtdjISKi2MYAIRERERERkb80A3hfyL/fG/jeHFLKNVLKBVLKBa6NjIiI4hIDhERElFDYFZKIiGJADoAPCSHeL4R4J4DvAHjL4zEREVEcY4CQiIgSBrtCEhFRLJBSTgL4JYCDAMoBbJdSlno7KiIiimcXez0AIiIiF812hQQAIUSwK2SZp6MiIiIKI6X8f+zdd3hUVfoH8O9JIwm9RLoGFVREsaAu9t7Xsrpr2V3rquv623XdoqhrdxULiCK6ImIDFBtYgtQk9BASIAkhhRDSe09mMv38/kgmJmFmMuXO3Dsz38/z+EiSmXvfO3PLue8957xrAKxROw4iIgoP7EFIREThZMCqkJzwnYiIiIiIwg0ThERERL1wwnciIiIiIgo3TBASEVE4casqJBERERERUThhgpCIiMIJq0ISERERERH1wyIlREQUNqSUFiGEvSpkJIClrApJREREREThjglCIiIKK6wKSURERERE1BeHGBMREREREREREYUxJgiJiIiIiIiIiIjCGBOEREREREREREREYYwJQiIiIiIiIiIiojDmU4JQCPFbIUSuEMImhJilVFBEREREREREREQUGL5WMd4P4DcAPvDkTWOHxWLFA+dgWGw0jk0YjKK6DhwzejCGxUbh5/01+MvyPbhg6hjceNpE3HrmJACA0WLFoKhIGC1WFNfr8PCyTJQ06rHgttNw4bQEVDZ34sZF23DNjPH4z/UnYWR8DEob9Rg3PBY2m0RGaTNGxEfjtMkj0G6wYPCgSJQ06HHze9tx1cnjsGpvJV644WRcM2McRg6OQX51O9bmVuPfV50IndGC2OhIWGw2dBgseGVNPobGRuHucxMxLDYKgwdFoaCmHUNioxATGQGjxYo2gwUbDtTi/dRDyHruSkQIIC46Evk17Xjo80x8fv/ZaDdY0NBhxLnHjUF0pEBU93t1RiuadCZMHhWHCCFgMFvxv82HcONpEzFt7FAU1LQjOlLg2IQh0BktiIuORHGDDlPGDIbeZEFkhIDeZMXI+BhYbRIxURG4dF4qiut1yHn+Stgk0NhhxOBBUfjDkl04WNcBAMh78WrkVLYiNjoCN7y7HTMnDcfzN5yMuJhIREdGYOXucizeUowbZk7AU9eehEadEcvSSmG1SXyVUYGc56+EEAJREV0xD42NRkmjDmarDdUtBtz7yW4AwHu/PwPXnjIeALDxQC1W76vEu3ee0WcfaegwQkpgaGwUVu4ux/lTx+DoUfF4c10B4mIi8ffLp0FKidyqNuhNVhgtVpx//Bg06UxYl1sLAHhqVQ4A4PtHzkO7wQKzzYajR8VjaPf31NZpwZDYKNikhMFsxaSR8QCAFr0J63JrEBMVgdzKNgweFIXbz56MmMgIyO6YDWYr6juM+OulU2GTEhFCYOehRiTn1+Gk8cNwxfSxkFLiqGGxSM6vRW5lG+46NxGDoiJgsUk0dZggBPDm+gK8dsupEAJYu78GX6SXoUVvxtd/no0hg6IgJdButGDFrjKMGRKDkfExuOiEBLR2mmG1SYwdFgspJZr1ZtS3GzE8Lho6kwUfbTuMp689CR1GC855ZRMA4PCr1yKvuh0njBsKncmCpdsO44KpY5A4ejBGDY5BWZMe24sacd0p4yEigGGx0ahpNWDU4Bi06E0YFheNQVERKGnUw2y1YdvBBtxzbiKWp5fh4mkJGDk4BnHRkXg56QC+yahAu9GCf1wxDVGRAkcNjcX5x4/BuOGxAACrTSKjpAnLd5Xhgqlj8NtZk2G0WFHfbsSilCJMGB6H+86fgiadCQlDByEqQqBJb8LI+Bh8vP0wCmo68Mz1J2FYbNf2xkVHIioyAnVt3fF2mpFR0oTjEoZgypjBMFhsiImMQHVrJ+atL0RcdCRuP3sy7v1kN378v/OxJqcaN50+EcPjolFQ047ICIGHPs/EqkfOxejBgwAAepMFf1m+B8Nio5GUUw0A+Pbh2Th98kiYrDYU1+tw7TtbccHUMfjvTadg3PBYtBvM+Cm7GiPio/HrUyeg02xFekkT3tpQiLduOw3HjhmMtOImnDhuKAYPioLeZMGI+Bi0dpqRX92G3y/ZhVduPgUdRgvKm/X4eHsJVvzpHJwyaTgGRUWiSWfCr17t+n6LX7kWVinxweZD+NMFx6Kt04z3Ug/hkx0lyHruShjNViQMHYSGDhM+TyvF8LhoJI6Ox7rcGvzpgmMxdmgsBg+KRGSEgOg+76zJqcbNp0+EEALz1xfgneQizP3NKbjtrMkQQqChw4g/f56JjNJmXHbiUYiLicRP2dXY+vglmDQyDm0GC9oNZsRFR2LwoCjc+WEaxo+Iw8MXHYdhsdGYNDIOTXoTBsdEIS4mElJKHKrX4fijhkBvskBAIP41T64uRERERERE5A0hpfR9IUKkAviXlDLDndfPmjVLZmS49VJSiNUmIaVEVGTfTqNLthbj5aQ83HNuIp6/4WSXy1ixqwxPrcrBHWdPxqu/OdWf4SoicU4SgK7ESUSEUDma8DJvfQEWJhfhH1dMw98um6p2OIo7b24yKls6sfXxSzB5VLyqsbToTWjUmXBcwhC/r6vdYMbQ2Gi/r6c3IUSmlFK1HuqDxk+V4+9eoNbqiYgoSJS+dr2q1yuA1ywiInKPs2uWrz0I3SaEeBDAgwBw9NFHB2q11C0yQgBwniSLdCOBZutJJjPZRqQVI+JjMCI+JiDrCnRykIiIiIiIiAJjwAShEGIjgHEO/vS0lPJ7d1ckpVwMYDHQ1YPQ7QhJM0R3XjCSpW2IiIiIiIiIiELGgAlCKeXlgQiE1OXOSPNbzpiE3Ko2/OvKE/wfEBERERERERERBUTAhhhT8IuNjsQrN5+idhhERERERERERKQgnwaLCiFuFkJUAJgNIEkIsU6ZsCjQJDjqm4iIiIiIiIgoHPnUg1BKuQrAKoViIRUIwYIjREREREREREThjOUmiIiIiIiIiIiIwhgThATAvSIlwYqdJImIiIiIiIiInGOCMMwxd0ZEREREREREFN6YICQi8hJ7pxIREREREVEoYIKQiBQXykPWiYiIiIiIiEINE4RERERERERERERhjAlCIlIch94SERERERERBQ8mCImIiIiIiIiIiMIYE4Rhjj29iDx31+xjAAAj42NUjoSIiIiIiIjId1FqB0Dkb4JZUFLYQxcdh4cuOk7tMIiIiIiIiIgUwR6EREREREREGiGE+K0QIlcIYRNCzFI7HiIiCg9MEBIREREREWnHfgC/AbBF7UCIiCh8cIhxmBs7LBYAMGFErMqREBERERGRlDIP8HyanFMmDkfG3OvQ2GHE6CGDYLbaEB3Z1R8krbgRMyYOh9FsRVWLAUcNG4TWTjPGDY+F1SqRV92Gc48fAwAorG3HtLFD+yy7w2hBXHQkIiPci0lKCaPFhie/y8HsY0fj4hMScNSw2CP+Hhsd2fM7i9WGqEjH/VcMZisAIDY6EjkVrWgzmHFed7z9VbV0YsHGQlw4LQGjBsfg3OMcv85gtuKvX+zFM9dNx9Gj451uy7K0Upw6aThOnTQCFqsNG/NqcfaU0Rg12L25qM1WGyKEQGSEQHVrJ0wWG44ZPbjn7x1GC5Lz63DDzAluLa83k8UGAJCQMFslShp0mDFxONKKG2Gy2HDhtAS3lpM4JwkAUDL3uiNir2juREp+HY47agjOO240WjrN0ButSBg6CHExXd/H6CExmDAizuGy95Q1Y0RcNI5NGNLzu20HGzAsLgqnThoBALDaJNo6zdhcWI+/r9zX87q/XTYV/7hiGgCgurUTI+NjevaZQ/Ud2F/ZihtPm+jWtkgp0aI3o8NowfjhsWjSmdButCBx9OAj9uv6diNeX5uPl26a0bM+q01CStmzj9a3GzFqcAwsNhtKG/WYNnZon3Wn5Nfh2ITBfb7r/vJr2vDxthLcd/4UpJc0YWb3fuaMwWxFSaMOJ44bdsS2pRbW46KpCYiIEJBSAvDsHGIwW5FV3oJzjh3d5/edJitMVhuGDIrq8znVtRsQHxOFDoMFI+KjMSgqwqN12j+rzP9cDp3Rismj4ly+t8NowZfpZbjvvCk49qk1AIA9z1yBimY9Tp00AjabRH2HEWOHxcJmk+g0WzF4UN90l8liw2tr8/HYFdMwpNffNh6oRUmjDreeOQkjvJxj3mSxIauiBWcljgIArMmpRnWrAfefP6XP68oa9dhf1YprTxkPm03iy93luH7meAyLje55za3v78AT15zYsywA0HWfgyP67atSSszfUIjyJj3m3nJqn3NqfxXNenybWYm/XXY8Il5z/Bph33kCadasWTIjIyPg66UjSSmxKa+cM+ilAAAgAElEQVQOl5x4lNsX/GDh7OJA/jdvfQEWJhfhH1dMw98um6p2OBTEhBCZUkrVhlcNGj9Vjr97gVqrJyKiIFH62vWKX6+EEKkA/iWldOvGifdY5C0t3Tf9/cu9WL2vCkDfBKG71NwWLX2OWufLZ8XP2XfO7rE4xDjMCSFw+fSxIZccJCIiIiLSKiHERiHEfgf/3ejhch4UQmQIITLq6+v9FS5RwDx88fG//KBCZyaicMYhxkRERERERAEkpbxcoeUsBrAY6OpBqMQyidR0wriheOzyaXhrY6HaoRCFHSYIKWRdckICUgr4JJWIiIiIiChYSDDXHepevPFklDTo1Q6D+mGCkELW+384E/XtRrXDICIiIiJymxDiZgALASQASBJC7JNSXqVyWESB52GhHgoed81OVDsEcoAJQgpZsdGRmDzKeUUyIiIiIiKtkVKuArBK7TiIyDNXTh+LoroOtcMg8hoThESkOM4nTERERERE4WTxXYoWMicKOFYxJiIiIiIiIiKioHDdqePVDiEksQchESmO04UQERERERGR0krmXqd2CCGLPQiJiIiIiIiIiIjCGBOEREREREREREREYYwJQiIiIiIiIiLShOjIrjRFVATnLSIKJM5BSEREYUEI8VsAzwM4CcDZUsqMgd5z9Kh4xMdFo7XTjL9cfBzOPW4MJo6Mg8liwxfpZdh6sB5/u2wq5nybgxPGDcW+8hYAwF2zj8FnO0sBAEvvmYUthQ34w6+OQZPOhGFxXZfe6hYDknKqMfWoIXj153z8euYE/OaMicgoacKilEP4+s+z0WmyQgJIHB2PtftrcMYxI/Hb/+0EANwwcwL+dtnxyKtux5Pf5eC935+Bu5amAwAW//FM1LQZkFpQj+MSBuOEccNw/vFjMCI+Gic+sxYzJg7Df66bjke/3IsFt52OY0bH49y5yZh97Gh8dM8s5FS0YmhsNBp1Rvzxo3SMiI/Gpn9chMqWTkwYEYeI7olGZ7+6CUaLDW/dNhPTxw/HkNgomC02WGw2DI2NhgBQ2dKJm9/bAQD45N6zUNtmwJQxQ/C7D3bi6WtPwhnHjMCKXeX455XTkFPZiqqWTkwbOxQPfpaBIbFRqG0z4rTJI/D0dSfhue9zcaC6DR/eNQtJ2VW4//xj8et3twEA/nbZVJx73Gjcvjit5/tLGDoIC247DfvKWzB+eCxOGj8MsdGRkFKizWDBhBGx+Hh7CVo7zThl4nBcOC0BkUJgwcZCREYILN9V1vN5Ths7FJfN34yjhg7CkEFR+OeVJyA2OgJZ5a34/a+ORnWLAZNHxWFPWTOe+DYH5x03GtsPNeLH/zsf7QYzRsTHIP1wE04YNxT3fpKOa08Zj92Hm3DVyeOwJqcaWRWtPXFfckIC7j1vCkYNjkFVSyde+PEAEsfEY3tRI86eMgoThsdi9b4qDB0UhXd/fwa+zijH/116PD7fWYpbzpyEtk4z4mOi8LsPdjrcr1c/ch5MFhuWpZVi+oRhuHBqAgYPikRxvQ73fbobH/zhTBx/1BBsyqtDg86IyuZO/JRdDQBYdv85AIAFGwvxx9nHYOvBBnyTWQEAiI2OgMFswwVTx2DuLaeipEGH2ceORkpBHY4eFY8r3toCAPjh/85DZIRAVnkrRsZH4+wpo7AsrQzvphyE2Srx8k0zsHZ/DYbHR+PWMyfh3o93Y9LIOFx98jiMGx6LE8cNQ2WLHu9sKsL950/B9TPHo7ypE1UtnRg1OAa/X7ILADBlzGAcbtD1HC8Xn5CA2OhITBwRh21FDXhjXQEeuGAKmnRmnD1lJFbsKsM95yXisZVZPZ/VsWMGY/qEYfgpuxpL75mFscNiYbVJ7CtvQX5NOx684FgcrOvAzEnD8e9vsvHGrafih6wqTBs7FEaLDQ981nWa2fzvi7F4SzGmjR2KC6aOgd5kRVVLJ9bm1mDa2KFoN5ixKOUQAOCNW0/FzkONeOKaE3HHh2kortfh1zMnIDmvFjqTFQDw1UOz8W1mBY4/aghOGj8Ms48bjQ0HavHnZZm4e/YxaNCZcMsZE9HaacbOQ424+ISjkDh6MEYOjobJYoPOaMWGA7V4a2MhAGD7nEvx2Mp9SD/chJMnDENuVRsAYNGdZ2DiyDgMjY3C1sJ6NOpMWJhchMevPgHXzBgPo8WKqxds7Tne6tuNmD5+GBbeeTrSDzfh852luPvcY5BX3Y7dJU3IrWrDxSck4K3fnYZmvQnVrQYcMzoeu4qb8M+vs3D60SPwzytOwH2f7sZzv56OhZuKMHZ4LOb9diaadCb87oOd+O2Zk3DZSUdh9rFj0Kw3YczQQZjx3LqeY1VvsuKbzAqcM2UURsRHY1B0JH41ZTT+9XUW0kuaer7bfc9egYYOE3KrWvHDvipsK2qA0WLDz49egFfW5GHrwQbcPfsYnHv8GIwZEoMfs6qRMHQQzjt+DOraDBg7LBbJ+XV4e9NBh8cZEXnvvvOmoFlnwgMXHKt2KERhRUgpA77SWbNmyYyMAe/LiChIzVtfgIXJRfjHFdPwt8umqh0OBTEhRKaUcpZCyzoJgA3ABwD+5U6C0NPrVWmjDoW1Hbhi+lgcqGpDp9mKM48Z6X3QHmozmCEADI2NDtg63VFY2w6z1YaTJwz36H2tnWa8sS4fT187HXExkWjVm1HSqMPMySN6XtOiN2FjXh1uPXMSAKBZZ0J9hxHTxg71Oe41OdW49MSjEBsd6fOyXEkrbuxJbG547EJMdRJ7SYMOiWMGAwD2V7bi5AnDIFxUhWrSmTAoKgJRkQIn/GctAOCpa0/Egxce53GMS7YW45pTxmPiiDiP32u3cncZ9CYr7j1visO/lzXqsa2oAXeec7TX6+hNb7JgS2EDrp4xzuP3VrV0YlNeLf44O1GRWNxhsdpgtkrExTjf3xLnJAEADr1yLSIV6FljMFshJRAXEwmTxYbaNgMmj4pHXbsBAHDU0NgBl5GSX4cR8dE4/eiBz3V1bQYkDB10xH6781Aj7vgwDedMGYWVD812Ge+gqIgj3p9X3Yaxw2IxanCM0/eaLDa8l1qEP1903IDHtNUm0WGwYHj8wOfSTXm1GB4XjbOmjFbseuUt3mORt+znllAo/hBK20LkL87usdiDkIiIwoKUMg+Ay4SKr44ZPRjHjO5K4EyfMMxv63FmmMYSg3beJuuGx0Xj5ZtO+eXn+GjMjB/R5zUj4mN6koMAMHJwDEa6SBJ44tpTxiuynIEMiuoaSjVz8ginyUEAPclBAJgxceBkq6NkiTfJQQD4kwK9OG47y3Xi7+jR8bhztDLJQQCIj4nyKjkIABNGxAU0OQgAUZERiHIzF63UWax3oiwmKgKTR8UDcC8xaHfJiUe5/dqjhjle7uBBXXGMjHd97DpL7J00fuDzbUxUBP5++bQBXwcAkRHCreQgAFx20li3XkdERKR1TBASERH1IoR4EMCDAHD00colK4iIyLFTJg7H3N+cgmtmBCYpT0REREfyqUiJEOINIUS+ECJbCLFKCDFi4HcRERH5hxBioxBiv4P/bnR3GVLKxVLKWVLKWQkJCf4Ml6gvFaZ9oeDkx47QqhBC4Pazj3a71x4REREpz9cehBsAPCmltAghXgPwJIAnfA+LiIjIc1LKy9WOgchT/hz2bnf7WZPRrDf5fT0UGIHYZ4iIiCi8+JQglFKu7/VjGoBbfQuHiIiIiJQ295ZT1Q6BiIjI7z66exYadXwgRuQNJecgvA/ASmd/5JxORESkJiHEzQAWAkgAkCSE2CelvErlsIiIiIhIISwcROS9AROEQoiNAByVgHtaSvl992ueBmABsNzZcqSUiwEsBoBZs2Zxkh0iIgooKeUqAKvUjoOIiIiIiEhrBkwQDjSfkxDiHgDXA7hMSs6uTUREROQJ+2xynFeOiIiIiNTiaxXjqwE8DuAGKaVemZCIKNhNHBEHABg/PFblSIiItO+UicNx//lTsPCO09UOhTRu+vhhaodARCHqxHFD1Q6BiFTm6xyE7wIYBGBD91PvNCnln32OioiC2m1nTca44bG4aFqC2qEQEWleRITAM9dPVzsMCgIrHjgHhxt0aodBRCFm1V/OxZQxg9UOg4hU5msV4+OVCoSIQocQAhefcJTaYRAREYWUEfExOP3oGLXDIKIQc/rRI9UOgYg0wKchxkRERERERERERBTcmCAkIiIiIiIiIiIKY0wQEhERERERERERhTEmCImIiIiIiIiIiMIYE4RERERERERERERhjAlCIiIiIiIiIiKiMMYEIRERERERERERURhjgpCIiIiIiIiIiCiMMUFIREREREREREQUxoSUMvArFaIdQEHAV6ysMQAa1A5CAaGwHaGwDUBobEcobAMQGtsRCtsAAMdIKRPUWnmIXK+A0NgfQmEbgNDYjlDYBiA0tiMUtgEIje1Q9XoFhMw1KxT2BSA0tiMUtgEIje0IhW0AQmM7QmEbACfXrCg1IgFQIKWcpdK6FSGEyAj2bQBCYztCYRuA0NiOUNgGIDS2IxS2QSOC/noFhMb+EArbAITGdoTCNgChsR2hsA1A6GyHBgT9NStU9oVQ2I5Q2AYgNLYjFLYBCI3tCIVtcIVDjImIiIiIiIiIiMIYE4RERERERERERERhTK0E4WKV1qukUNgGIDS2IxS2AQiN7QiFbQBCYztCYRu0IFQ+x1DYjlDYBiA0tiMUtgEIje0IhW0AQmc71BYKn2MobAMQGtsRCtsAhMZ2hMI2AKGxHaGwDU6pUqSEiIiIiIiIiIiItIFDjImIiIiIiIiIiMIYE4RERERERERERERhjAlCIiIiIiIiIiKiMMYEIRERERERERERURhjgpCIiIiIiIiIiCiMMUFIREREREREREQUxpggJCIiIiIiIiIiCmNMEBIREREREREREYUxJgiJiIiIiIiIiIjCGBOEREREREREREREYYwJQiKVCSE+FUJUCiGMQogGIcRaIcTpasdFRERkJ4T4uxAiWwhhFUJIIcTzasdERETUnxBiiRAiTwjRIYRoFEKsEULMUDsuomDABCGR+o4BsBnAUgCNAK4CsFrViIiIiPo6E0ATgHK1AyEiInLhfgAtAL4A0AbgGgBrhRCxqkZFFASi1A6AKNxJKS+2/1sIcQaATACThBDRUkqzaoERERF1k1L+EQCEEKvR9WCLiIhIi2ZJKTMBQAiRCOAwgIkApgPYo15YRNrHBCGRBggh/g9dF63Lun81j8lBIiIiIiIi99mTg91iuv9vBVCtQjhEQYUJQiJtuBXARd3/rgCwXcVYiIiIiIiIgpYQYgiAj7t/nC+lZIKQaACcg5BIA7qHGccBuAnABADfCCE4hIuIiIiIiMgDQogEACkAzgXwIYAn1I2IKDgwQUikIiFEnBAiEgCklAYAawF0oKt377FqxkZERERERBRMujtZbAMwC8CrUsoHpZRS5bCIgoLgsUKkHiHExQBWANgCoBnABQBOBlAPYKqUslW96IiIiLoIIf4E4HwAlwKYDCALwD4Aq6WUq9WMjYiIyE4IUYmuEVllAFb1+tMKKWW6OlERBQfOQUikrioAhQCuADAUXYnBrwG8yOQgERFpyPkA7u7188zu/0oAMEFIRERaMaH7/0cDeLTX7/cBYIKQyAX2ICQiIiIiIiIiIgpjnIOQiIiIiIiIiIgojDFBSEREREREREREFMaYICQiIiIiIiIiIgpjTBASERERERERERGFMVWqGI8ZM0YmJiaqsWoiIgoimZmZDVLKBLXWz+sVERG5Q+3rFcBrFhERucfZNUuVBGFiYiIyMjLUWDUREQURIUSpmuvn9YqIiNyh9vUK4DWLiIjc4+yaxSHGREREREREREREYYwJQiIiIiIiIiIiojDGBCEREREREREREVEYY4KQiIiIiIiIiIgojDFBSEREREREREREFMaYICQiIiIiIiIiIgpjUWoHQEREFEhCiBIA7QCsACxSylnqRkRERERERKQuJgiJiCgcXSKlbFA7CCIiIiIiIi0I+SHGUkpIKdUOg4iIiIiIghzvK4i0jzkAIu+EfILwno93Y8qTa9QOg4iItEMCWC+EyBRCPNj/j0KIB4UQGUKIjPr6ehXCIyIiLVq9txJTnlyD8ia92qEQkQtLt5dgypNr0KI3qR0KUVAJ+QTh5kLe3BERUR/nSynPAHANgEeEEBf2/qOUcrGUcpaUclZCQoI6ERIRkeb8mFUFACioaVc5EiJy5avd5QCAmjaDypEQBZeQTxASERH1JqWs7P5/HYBVAM5WNyIiIiIiIiJ1KZYgFEJECiH2CiF+UmqZREREShJCDBZCDLX/G8CVAParGxUREREREZG6lKxi/CiAPADDFFwmERGRksYCWCWEALqugSuklGvVDYmIiIiIiEhdiiQIhRCTAFwH4L8A/qHEMomIiJQmpSwGMFPtOIiIiIiIiLREqSHGCwA8DsDm7AWsCklERERERERERKQ9PicIhRDXA6iTUma6eh2rQhIREREREREREWmPEj0IzwNwgxCiBMCXAC4VQixTYLlERERERERERETkZz4nCKWUT0opJ0kpEwHcDiBZSvkHnyMjIiIiIiLSGKl2AERERH6g1ByEREREREREIUsItSMgIiLyH0WqGNtJKVMBpCq5TCIiIiIiIiIiIvIf9iAkIiIiIiIiIqIBLdlajMQ5STCYrWqHQgpjgpCIiIiIiIiIiAb0wZZiAEBbp1nlSEhpTBC6IKXEvPUFKK7vUDsUIiIiIiIiIhqAZCkhIq8wQehCTZsBC5OLcPfH6WqHQkRERERERERuEmBlISJPMEHogux+8GCx8gkEERERERERERGFJiYIiYiIiIiI3CQlOw8QEVHoYYKQAsZgtqK0Uad2GEREREREXuBwRSIiCl1MEFLAPLJ8Dy56IxUWq03tUIiIiIiIiIiIqBsThBQwmwvrAYA1pYiIiIiIiIiINIQJQiIiIiIi0qzEOUl4ZMUetcMgIiIKaUwQEhERERGRpiVlV6sdAhERUUhjgpCIiIiIiIiIiCiMMUFIRERERERE1A+LKxJROGGCkIiIiIiIiKiXorp2HP/0zxzeTtSPZNXRkMUEIRERERERkZt4bxwe9le2AQDWH6hRORIijRJqB0BKY4KQiIiIiIhoAII3w0REIam+3YibFm1HbZshoOud8dw6LN12OKDrdIUJQiIiIiIKuNyqVhysbVc7DCJyQ02rAWWNerXDIHKLGkNg9SYLOoyWwK+YFPFFehn2lbdgWVppQNfbYbTgxZ8OBHSdrkSpHQAREYW3v32xFwDwzh2nqxwJEQXSde9sAwCUzL1O5UiIaCC/enUTAB6vFFwC2ev3jJc2wGC28RihoMYehEQUsrIrWpA4JwnZFS1qh0Iu/JBVhR+yqtQOg4iISDOEELFCiHQhRJYQIlcI8YLaMWlJu8GMz3aWQLJaAmmEwcyK1xT8mCAMYgU17fg5J/iqavE6ToGyMa8OAJCcX6dyJEREREQeMQK4VEo5E8BpAK4WQvxK5Zg047kfcvHs97nYeajR4d+llCio4RQGRESeYIIwiF21YAseXr5H7TDcxomdiYiIiIgGJrt0dP8Y3f0fH7N3a9aZAAAGi9Xh35duL8FVC7Ygo6TJ53VptXNDeZMem/Jq1Q6DiEIIE4RERGHq64xyNHQY1Q6DiIiIHBBCRAoh9gGoA7BBSrnLwWseFEJkCCEy6uvrAx+kRu2vbAUAlDV5X1hF650bLp+/Gfd/mqF2GEQUQpggJCIKQxXNevz7m2w8vCxT7VBU0X3TtVcI8ZPasRARETkipbRKKU8DMAnA2UKIGQ5es1hKOUtKOSshIcGt5dpsEj9mVcFm87xrnMliwzubDsJgdtxzjwLHaOGcd8GgqqUTK3aVqR0GkVuYICQiCkNma9dNQX172PYgfBRAntpBEBFR8An0kFMpZQuAFABXK7G8lRnl+OsXe7FsV6nH7/1sZwnmbyjEkq3FSoQSMCUNOrQbzGqHQWHorqXpeGpVTs+weAqc7/dV4tJ5qSxm5AEmCImIKKwIISYBuA7AErVjISJSU2OHESUNOrXDIAeEEAlCiBHd/44DcAWAfCWWbX846M1DQnvPwc4g60F48ZupuH1xmtph+EVacSPW59aoHQY50dSdGLQxSRVw//wqC8X1Oli86C0dCvaUNXt8nmeCkIiIws0CAI8DcDg2h/M5EVG4mD03GRe/map2GEEnQHPTjQeQIoTIBrAbXXMQcloMH+RWtXn0+mDJ59y+OA0Pfh6eU8YQkXO/eW8Hrl+41aP3MEFIRCEvWBp45H9CiOsB1EkpnbakvZnPiYgoGJk4h5lmSSmzpZSnSylPlVLOkFK+qHZMWhSINp7Wi5X4i8liw1+/2IvDHvQy/iazAolzktBhtPi8/madCYlzkrDci6Hw5G+8uQoWtW3sQUhEBAAYqD1346Lt+Gp3eUBiIc04D8ANQogSAF8CuFQIsUzdkIiIiMgTIlyzdgGUWdqMH7OqMOfbbLff835qEQCgprXT5/VXNHct44t0FvjQKjHg3VbwYIeSLkwQEpFf7ChqwKq9FWqH4VJWeQse96DRQ8FPSvmklHKSlDIRwO0AkqWUf1A5LCIiItIoJg5Iy3RGC8qb9GqHoWmeHMOhk/L0jiYThK16MxLnJGH13kq1QyGN+M/qHGwu5FxgweTOJbvw2MostcOgAbDNS0REFLqklJj7cz5KG1mMxlPB2EnRYLZiydZiWMO0KEM4uuPDNFzweoraYWhSMB7DatNkgrCk+wK2dPthlSMhrViWVoa7l6arHQYFGTaNnOP1EpBSpkopr1c7DiIiIn853KDD/zYfwp8+zVA7FEVJdutzaFFKEV5OysO3e9QdxfPWxoOqrj+cZFe0qh1CUHryuxyc+MzPaoehOT4nCIUQsUKIdCFElhAiVwjxghKBDaRFb8JdS9M9LttM6pNM21CA8ekRERERKSWYclP2UEO1RxnbeH21dZoBAHo3i4QU1XV4VITEXUnZ1Yovk6i3J7/LxkOfe//g44v0MhjMyhfq+mT7Ycx4bp3iyw0UJXoQGgFcKqWcCeA0AFcLIX6lwHJdWr6rDFsK6wPayzApuxrL0lhFyVuhNIkpAbVtBrVDIKIQkjgnCf/+mtMSEJF2sSUbfIIpmWswW5FV3hLQdV4+fzMueTM1oOuk0FZQ0w6jxer39XyRXo51ubV+X4+nnv/xgCJVvNXic4JQduno/jG6+z+vTsWr9lYgs7TJ15D85pEVe/Cf1fvVDsOpDqMFthB9Okja8v2+SpzzyiakH3b/eDVZbKhs8b2imTeCqXEYKPxISIu+ztR2YSMiItIWZ228YEzmPv5NNm5ctB11QfwQ3h/tS1ft+LTixqDpHRscUfqmvt2IqxZswX9WaStn4usIxh+zqtBuMCsUjbYpMgehECJSCLEPQB2ADVLKXQ5e86AQIkMIkVFf77jYxGMrs3DL+zuVCEkR3uxGVy/Ygo+2dfVqPNygw4GqNmWDcqK104wZz63DvA0FAVkfhbeMkmYAQH6N+/v3499k4by5yeg0+f+Jkl0wNg4DjZ8REREReUrph696k2c9bkQIji3OrujqPajr1VZu1ZvDdkqtgb7iHUUNuH1xGt5PLQpMQF5yd0996acD+HxniR8jUV5rpwmJc5Lwc07XkHJ7Ei2jtFnNsHooMYIxv6YNf/1iL574NluBiLRPkQShlNIqpTwNwCQAZwshZjh4zWIp5Swp5ayEhAQlVhswnuxW+TXteOmnAwCAS95MxbXvbPVPUP206E0AgB+zON8DaVNyfh2Arp6EWrSvvIWVsomIiCgkOcrneZPk80diLr+mDdOfXYfv91Uqvmwl9P+YXlubj8Q5SQFZ91n/3Yiz/rsxIOsKNjXdPS0P1YdGhe6Pth3GM9/nqh2GR4rqugaSLtkWusVl9d0J+6qW4O3Z6wlFqxhLKVsApAC4WsnlEg2kVW9WLLmTVtyIZp1JkWWFm/ImPRLnJGm2gefKTYu2s1I2ERERhRRH6Tyt9b2zj7j6bGcpWju1MYzvQFWb01Ev76ceClgcJqs2H6wTaZXJYsOSrcUw89jxihJVjBOEECO6/x0H4AoA+b4uF+C8YeS+mS+ux91L031O7EkpcfviNNzxYZpCkYU2KdFn2ENedVcDjz1ZA+PiN1Jw4espPi2Dp1kiIiLSgszSZly/MDCjr1zRGS249p2tmPNdjtqhaMKuw01IK24M+HqZCwBSC+pQVNeudhiKs9kkzpub7JdOJR9tO4yXk/JYXNZLSvQgHA8gRQiRDWA3uuYg/MmXBYbglBIUIEo9KcivCb0TsT98sqMEZ/13Y0/3cgqskkY9ypr0Xr2Xp1kiIiJSm9Um+ySCypvUKWjXW//pcHxtM1msNlz3zlakdE+3Eyx635Pfvli9zhNayA1MfXoNFqUEfq7Dez7ejcvnbwn4eu02HKjF/spWxZdrsFhR2dKJOd8qn4S3z4Oo79UD2J1ks6+FTLTAapM+F+1RoopxtpTydCnlqVLKGVLKF31dJpES/vlVFk55bp3aYaiuptWAxDlJ2JSnfBn4ww1dc36Ue5mkIiIiIqLwVNtmwHFPrcGK9DK1Q/GrZr0ZuVVt+Pc3WS5fF/zpidBltkq8sS78ioE+8FkGrl+4Te0wvOdNclkLGWkvXfJmKk56dq1Py1B0DkKlaKU7sT377M9wpJT4bGdJT5GRcFFY2471uTV+Xce3eyrQbvSsIloosldE+yK9PIBr1chBTERERKQ4tnOUUNrY9YA504eKp1q5b/SUlBLvbDqI0sYjC2wEb3oCfjk0gvU7Die2EPyS9le2InFOEsoag6cjTFmT3ueCoJpMENqpmbxt7DDi6gXKzIFhMDue4BYAsita8ez3ufj3N+FRNtvuyre24MHPM9UOIyAKatph87Grb7DwR2W7UDNvfQEeWbFH7TCIQq7KgncAACAASURBVMY3mRW4ffFOtcMgojAQLM0cR/ceMkhv4G02iZd+OhBUN+nuqG0zYv6GQlUL5NnnDteqYDneCNhb1gJLiBUF+Tqjq3NNSoG2pwZo7TQjs7RJseVpOkGopoYO5Xr0nfiM826exu4Mr796EK7JqUbinCTUtQVHWe6Cmna0GbRRvcyVlPw6t4bV7itvwVULtuDDrcUBiIqCwcLkIiRlB28Rl2adSZWJqomc+dfXWUgrVq5hREQUzDYX1uPEZ9b23DDWtGrvHqC0UYe7lqZDbxp4pE9eTRs+2nYYf1nhuGNBIJJI/kit2ntc2e8FawN8r6Y3WXDN2+oXhAlFZqsNDR3GI35/qK4Dyfm+T/m0Ka8WrXrt3S9bwqRDjNbc98lu3PL+ThgtzjuleSLoE4Th8GChscOI53/I9aoAxxfdc3oES9GNqxZswR+W7FJt/e4+XL33k924dF7qgK+zJxGz/TC5q9KsNolVeyvCprcjeef3S3bh9sVpPk+AS0RERMrbXtQAAMgo6Rq2e2d3u7pDQ9PuvLomH1sK67G5sH7A19rb5jYvOycFQ2vlh6wqnPPKpp4h14Hg6zBErVPze//nV1mY9fLGI+6pbluchvs+yfBp2fXtRtz/aQYeXh68I/GeXpWD6c+yToBS7EVk1u5XZvo2TScIg7QnvOJe/OkAPtlRgnV+nrNPK7Ir1E+mufM00mwNrR10WVopHluZpchE0Wocu2+sy8e+8pbArzhIefsV5dV0DUcJh4czREREoSIc7qu+TC/DFfM3d/0QRA2VzJLw6AWvRlIyELtBk87UJwH/U3YVAP8kKe29xHYc0vZoHldTGizf5d69psVqw7z1BT11Ibyh5Pev5PdptFjx8fbDina4ePTLfYosR5MJQq3PN2C0WJEVwESEvbuuJxd2s9WGxDlJ2HqwwU9RUaixd4Vv0nk/3F3NQ3dRyiHctGi7ihEQEanndx/sxHQfK9cREQWzOd/l4GBdh9phhK2bFm1HccORRVcAILeqFdP+8zPW59bAYLbivdQir0bH9aaVpPcZL23AxW+kOv27VnIbP+dU+1QQyJH+34GS89H/kFWFhclFqleP9naLUl3MXbgouQgv/HgA3+6p8HLp/qPJBKHWvfjjAdy4aDtKnJwAtaDTRWEUooHk17Th+KfWoLKl0+/rKqrrcDhPR3/2+TS1OOdGMLJf7Eob9ahu9f/3TET+lX64CXoTr/1EA9lcWB80c3MrSelcii/LWxsmo6K8pZWEkqdcjeSx/y2loB7vpx7C62sL8OXuckXWq4WPy517GU9Ut3ai2YdOG448vHwPbnl/h6LL9Cd7AtlVwddA8PZcd8/Hu3HYSb6otbPrflavoakf7Jgg9IJ9nHdLZ2ATFW+uL3BrMt9wplTjRytPpNTyxa4yWGwSGwLQgLt8/mZc+HrKgK/7YEtXoZfiBs+fDJe5UVAmFOwrb8FTq3I8rlR46/usAOuL5PxafL+vUu0wiIjIDXcvTcct/wuem2SlOGobSEiUN+mDYv5pZ0mzQLTZ1+XWKF4FWvufuP/oupMiBjcfamnpvkxvsmD1Xv+3+Wa/mowzXt7g9/WEIqV2FyV6Q+qcJAA1tEsfISgShFJKfJle5vQD9sSh+g4U1QVHwY7+Shv1eGdTkV+WnVfd5tdeRHnVbTCFWOlzcs7Tk56/e72sCsCF3FedJqvPc7P8/sM0rNhVBp2Hnyd7EPrmvk8yFJv3Q211bYagrrLtrppWA6txE4Wx8qbwvu7Z73sLaztwwespPQ9hg4k/e431zwuYLDYsSyvFHz/ahTIvColIFy1jAWWHZYaiZr1JsQRtZmlTTyEfbzz3fS7+vnIfMgIwb2TvTd55qBFTnkxCS5iMpKprM6ChQ7kelFpKMvemxLGv9KYFRYJwd0kz5nyXg2e+3+/zsi6btxmXz9+iQFSeSZyThE93lPi8HH9N7nrN21sx+9Vkvyzbvvxg4LdzRwBOSlLKAS6efgyi+9ymtfaN1uJx5aRn1+K6d5Q5TvYHQdVsR6SUQdGLIZTduWQXHlmxB50hPlT1irc24/bFaWqHQUQBJKXEdwrN96TVm01PVTR3JUrTD/OByUC+3F2OrQcb8Nq6fB+Wol7DtKxRj8xSz5JaWmlGV7ca8HlaqSLLuuX9nfh9d2Vvd/V+cFrTPT2Bpw/jffW/zYcgJXD9wm0BW2ebg+IgJosNV761WdHknSNnv7JJkbkHnd0LPvBZ32rSV8zfjI+2HfZ5fUpasrUYB6raAr7eoEgQ2ofV+ntH9LeFyQfVDsErm/Jq3U44NHQYURQCkwN7mlgymK0O54kIZIJqypNrcOv/jhwq6umTiVBp9AYjpSbWvn1xGsqDcFj1y0l5OPapNUGbJCxp0GFPmbKTPwdaZffNoqveDqGg3cDpOojCzep9lfjHV1k+LUNoJmWiXVJKvLomD4W1yozY6t8unb+hEItSlB1RZTBbsU5D8yL6oy1+4RspuCWIp5TZXFCvynpb9CY8smKPosusa/9lDlSbTeL815Kxaq/vDy/2V7aiptXx/KrezLta2nDkvURlSycKa4P/Xn/Dgdo+Px+s6+hpGyp5lnc6xNjJMb4mpxrPrO7qFPdyUh6uVajziCeCIkHob1JKfJ5W6vQLrHZyoCkbg99X4bX7P81w+2nFha+n4PL5m/0ckfbcvjgNp7+k/jwRripTtXaanV40AN9OhmmHGlFQ80tD0N1hACkuqjuFmiVbixW5+LurdYA5Ug0W7fUQ+3h715M7DZ8OXbr4zVT85r3wm9eKiCgYNOvCY2ieM0pfW99PPeRwAv5GnQkfbCnGnR8O3Evbk/sf+/PudzYdVLyq6Str8vDQ55kBGTbaX7C2eYKRq0KHepMF/006cERBDLNV+W/o4WW/JBzNNhsqmjvxxDc5Pi/3+oXb8KtXNzn829mvOP69kvydzzBbbXg/9RCMLu5h9pY14/3UQz6tx74ZSlR8vm1xGg66eFjSvx/PX5bvUay3rLeCPkHYocC8hNuLGvHM6v144cfcnt8FqudX//VUNOvRove9p2T/8AN18QnXCorOqnZpKfG7u6TZ6UXDVx9sKcZVC7Z4dNzUtBpw78e7/RKPFr2clIfHVvrWc0FJb64r9Mtym3UmPPFNtuoVx8g9r6zJU7wnBhF556uMcqzdH/pzgFJg9G+COmqTOnqg+21mBRLnJLk11cTDyzKdr9/PbWAlF28fdeFoSKWdO21co8WKmS+sd+s4drU4f96HJs5JQmpBnabuUQLlr1/udfq3xVuK8eHWw/h4e4nP6xnoo20LcKHTULE8rRSvrc3Hhy7mTN1cqFxP05wBRlC+9NMBzPk2u8/vLA5qLjjqcbkpr6sHY+8EtD87kjTrTG4Xuw3aBKH9gvbZTt8zrPYPq8nDJ4vtLi4i3jr/tRRc9Eaq07+H+rAvR1xl3YNGEIxIcWfP+vXCbbhWofkkOwOQQArHxo+7Wjt/eRBhk10Nxt69QJ0Z6DN9Y30BVmaU41uF5nki/1q8pdhhTwweO0S+ya5o8XhI1+PfZOPPy/b4NIE+kTdNzt7T0SzY1PUAsb7dqFBErtar7jI2HKhVrGCVlBKVzZ1o7TTj8W+yB369Imt1z7aDfc8pK3aVeb2shg4jrEE6FUyFi+l3LN2JGkcJHl/1nyZNqSmFtCCQ02nZ5330Zv5HJXIo2RV9OwR9tO0wvtxd3ud3BjfrRVR1j+pr7PjlPOvPjiSnv7QBl81zb5Rn0CYIteCPH6X7ZbmtneY+T+2CILfkV/d+Ej69zLTA1Yk+p7IVB6r9O1nqprxaLNXIJLFKVE7XKkfzKG3Mq3Xwyi57y5r7TJTrbD9Rqsoc9XX5/M2KFbFxJZgK+6jlmdX78VW/BiFRfze8ux2XutkY70+JYU0AsGBjIRZv8W2oFYWWZp0JT36XDaOfih56yp05HQe6sfem2WG0WPHYyn144LMMxQpWPfFtds8x3+bBPLeBmMrK2yRo/8RWfbsRs17eiHnrPR/iHU7NQzaltEPJeWP9sQ+70+7eeWiA49fNuNw91zBB2Etpow6Jc5Kw3oNJalftrfDLk4aTnl3rU3IijM7BitJCcqOsUY/EOUkB6UFQXN+BDQdqUd3a2ef3vn4Kvrz//k8z8OJPB7x+f1mjMsU5Vu2twMnPrXOrV11vjR1GnDc3OSR6vjbpTD3z8dz83g63Jsq1H0KcyF1ZRXUdyFWhkpmSOk1WmDRyU+qLz9NK8fi3A/cOoeBgtUm8sibPqwncB6LENDi+WLDxIF5Z40vFVQolUkq8vq4AX6SX47s9lQB+qcgaDPq3K3xpZWwuqMeqvZU+rR8Arnl7a0+786sM90dOSCnx2c4Sj9bfm80m8f0+z+L3Rv/iHI26rt5Om/K0MYe4+ndt4cfdh9VqfTd+SeL1+zm/pg17A1iU8I7u+VwXbCzERW+kuPWe1II6JM5J8mp9IZUgzK9p69NN01P27r4/Zg8wb0SvVO9jK7OweKvzcfC+CKYqi616s9s7odo5OL3JMuC8KgJAVUsnbn5vuzK92TzY5vTuhEwghmheOm8zHvgsA7NfTe75Xe/vx9MKyL4khRLnJOGrDBe9ctzccS50ceL05GYtOb9rDov8Gs+SMhvzalHZ0okP/XReUIo7Xe1v/d8Oh5Wx3eFNT7RwbujtKm7EbhUmR1fKjkMNSJyT5HJI2knPrsWVb4VfESvStrTiRizeUsykL4WF/g/CW1wUbQgEV22RHYca8OrPeV4v254E9ae86jav2nupBfX4YLPz9+0q7moPOCvGsDy9DI9+uc/j9aqpd7vQnfZeu8Hs/nREbrY5lWxnVjQr0yHBLlimEetfb6D/7Zk/ugdordPB1Qu24mY/FyU0O+iAtmDjQZT26wjT2mmGycFr3/OhUEtIJQivXrAVVy3YEvD1NrQfWVTEVbXYQAj0YVSgkd5S7uSQpj+7DjOeX+d6OQDmbyjE3rIWvPjTAY97kdlpcbies3lDtBDqMjeqNnmStOz90g0HajHjuXWKDd1Siju9Vho6jGjS+V68yFPF9UdWJxyIEsVJtLAvBtpti9PwWy+TsVqwdFsJAAz4RLVEoR6+REqxSfu8U8FxcxZqzvrvRvz5c+eFLvxp68F6JA3UKcCJYNxbtBizq8//zg93uUyi2f3pswzF58Tzd2/3gYo62h9Y/Jjl+PMJxPyQnvom0/OODf2b9NWtnUick4RvMitwyvPrfWoXeTsqrP+7+sdY1N2h6OlV+71afn9qJb8O1Xfgvk92q15UsNNsRVVLp1tFkfwls9T9B/SOalC4s6+16s09+05vzr7/C1/v2+HFWScXpWoD9BZUCUJ3Pvz+cyWoxV/VYh3tRK5KtgeLrzPKve4G6w1PGxLBNCzOVel34JeboUDZUdTg8ClIoNmHbGc5qDit1mTL32ZW4OxXNg2YVJn18kac8dKGAZfnaY9Pf1i9rwpAV0L23o/9M0+rL4QQsUKIdCFElhAiVwjxgtoxeSJQQxa1eCNJFIp2lzTh4WWZsAXppP++qm83Yq0HU/so6Y8fpR8xhHIgGrjM+oVam1XnJNHV+57AnWZr/3amr0mPp1cfmfzx9LtPnJOEqpbOgV/oghJt9rJGvWKFWFz519e+F1iwV3t1tSwpJdbkVKOorh3J+c7nzrZz93tz93WBmDPSUTVtpa8Qz32fi+T8OpejVwLVq/Hcucn49bvbnP49q+LIe7f++Y+vM8rxjIPj1h33f5rh8u+9P4VTnl9/xN+/SP9lBFxxvYMkoABuem87Lp/v/iia/vvZmU7uAyudnGNcFeQZiKYThPadsv9N7/JdpXhzfaEaITlkj/O8ucmuX9e9d5mtNrdOaK7W1dvMF4/cUbVkw4GBt/WTHSX+D8SBwtp2FGqk96NSmj2sxu0Pvdszdy7ZhXkKHq+dJqviSfG3Nx1UdHm9lTbq8J2T4eL2BtvB2tCpZmaXnF+HlIJ6tcNwxAjgUinlTACnAbhaCPErlWNym6Mnl0oK0XvfgNlV3Oh1AzWQLFYb5m8o9Pv+RAN74LMM/Ly/Bq2d7n8X+ytb0axCr3J/6jBasGJXmSbmgg5ZCn20avU46p/AcZU8O/GZtT6tS6mEWvrh7uSLirv1hW+kIENjo2e8VVTXjilPrsFflu/B5fO3YE2OOg8XlFLepMe0//x8xO9PdZCE2lKoyTa1Yhz1rrPLdzCSb+7avvPr/vubbHyeVtrnUOt9zthe5L8keUGv6ahueHe7w9ccbnA8KsvdxLSnhaWqXCSyf86pdtkxS5MJwoG62irVpVdJe8uanWZw+3t740Hc90nGESXnnfnJy+EPWvHAZ66z8mq68q0tuPKtwA9LD6T+R9PWg/X4+5d7A7Oybo6eprhjf2UrEuck9en1d8eHaYonxXMcPJlSyg3vbsc/vvJf2XolWW1SEzdn/oxAdrHvkNHd//l9o7/fV4kdASg8FG78MSTFYLYOXDHOidsWp+FzN6ZKUNuP2VV4Z9NBvLHO80qUpL7rF27DTe85vgkJVs99n4unVuVgZwB6OvlCA5dI1S1MLnLrdf7+qJR8COmvWPt37AjVHqiuGMxW6E2WntE6/efuq2szuF3w8811zjsc7DjUoMp0PN7adrAB2RWtPi3D3w+KAj382ZOHD+7uM/Z5LF3lI/qf130pIBQMBuoYo8kEoa82q5Bhd2eiykadCRXNerybUtT98y/d6tneUNY+B8NI3RGo7+H91EMObyK3HWxwOARWSfd9ktEzDLS3d7obfGpOktv/MpSc31Ul7cZF25Hl40XUXY0dRkWSZPZqdp70CgkkRxf9+RsK3ZoHsr+vM8qRkq+NinbuEEJECiH2AagDsEFKuavf3x8UQmQIITLq65W5njz65T7cuWTXwC+kIxTUtDudBsAf56unV+3HHR+mef1gIxjYp81Qe+4h8l7/icqDnb1NbDSrPyWJO8Iwz+M2Z59Nq97scOikEnxttqV2JxvdSYgc9mB+ZmdxuXvt6l0oUc052nxx7txkTH92HTJKunoxbu3VQabNYMHZr2zCCz8eGHA5m1y0M6XsmrPy9x60s6w2iWwnHQTeSy1CiZMeX+5w59v9w0e7sLpXJWpv9mFXhRmDkVqddvrfqyldvEnpB0steu8Tw/d+nD5g20+TCUJfG/x3L033eU4xV5cHq837xsuWwr49SPzVwOg/LFsLvYI8MfXpNW4/GXDkpkW+PVkX6Hswe7tPOvvYX1ubj2dW78eilL5PYf/w0S6fe5/cuSQN5d3zDhysbceWgwFMmLvxMb2xPn/gF6nozJc3Yun2ErXDcCk5v9b3p4ZOTj6OkscD+fc32bj3k92+xRNAUkqrlPI0AJMAnC2EmNHv74ullLOklLMSEhJ8WteqvRVOhxVomVauGQU17bhqwRa8vTFw04ocrOsaytJuCMx8j0T9uXv4/ZwTPCNMcqtagyohvXpv5YDzA/ubEGKyECJFCHGge87cR/21rsMNOqQW9E3AuNP2za9px5LuKr5tBjPeTz0EZ7dgM19c73Do5EB6L86ba1NBTRv+sdK9ir9NOhPeSy1y2J61N5sOVLcd+Ucn3A3XUc/CRSlFfYZWejolUqfZ5nIYYaCu8q569XV0X2c35nk39Zadfb/oPdTT7pHlex1+Du8mF+GGd7cfcZw36Ux4fW1BT7JxoHt1XwrG+DpHpdbaKb7mcMp8mDfPbqDz9rrcGpz/mutp4fr73ot7I3d5kwvy5Z4rpaB+wIKBmkwQ2vnarXWXgsMUvu+V4d9f2ffko5H7KADBUyJ9IGarRHlzp2ZurG94dzta9CbUtxuR16th4PZ8EE52ZVfDu8q9PEkW1+swb33Xcq94aws+2+lZwtHf3cmXpZX5dfkDefGnAwPOEda/kawlrXoz7vskA39yc+i+xWrDwk0HoTdpqxGhBVLKFgApAK721zoeW5mFa95W7omov683Wih001ttd5XvvX7uWU3kSp6ThIDZasOSrcWqFeJ6eLlnBTbU0qwz4bp3tilSxCBQ/r5yn1ujg/zMAuCfUsrpAH4F4BEhxHR33ywlcKDql33X1eWjuEGHez7uuun0tB34clIeAOD5H3Lx2tr8I9pQTTqTIseIALwa5fDh1sP4bu8v93GNHc4TOk98m43X1xagPUAFwewcXdv73yPc6GHnh7p2z4ppLNx0EI+t3AeL1YYCB3O+Ab61QRy9den2ww5+67v/bT6EQ909PQucJFYPVHeNTLK3M+zsc1u6+0Dji3Tn9zVaedgaKC/9lKd2CH16qDryn9X7UdHsW2K2v0B/y4VOjk+laCJB+PwPuXjo819udp1N7uiJDoMFty1O8+q9jg7mR79078mTVvmzW7q/5j+45M1UXPJmql+W7Y2aNgMueTMV1/QqJ37X0nS3Ptuk7Grc5yTbnzgnqafCbm+7S5px9YItuOX9wDdQex8C63JrkOGiwlUfbrYp9SYLdI4aXwFKTrjTS7P3eUBK6TRhuyan+oj5VPzJ1N3ILm10L3n+3Z5KzNtQiLc3el+IpcaPFdsC3XgSQiQIIUZ0/zsOwBUA/Nqt1RAkQ+ZIG1o7zT71oCdl/ZBVhWve3uqwt96nO0rwclIePvbhJtfRKVBjeXqf6bofUO0tGzjR/9ra/JArwOItKWW1lHJP97/bAeQBmDjgG7v3n9ZOM659Z2ufP/nzkmvvDWa29l3JGS9twN/d7ME3kEMeDO915qoFzh/aWbwcgTblScc99YIpPTRvQyFW7e1qM161YAtuX7zT6Wu1fo56a4Pnow6U3CRvluXtvucNg9mKmjbl2/aukqWeWLXXcXFHrfpsZ6nL+f2VPl7se4q/7qE0kSD8ZEcJ1uX61rW4P6MleIYxOOOo7LiUXdWS73CS/LQ/9eu/w/R/yvxu8kEkzknqmYfIWyn5dTj9pQ3YFuST7+8oasDa/QMP1elwkNSyujg4e58QkvPrIKXEw8syj3ids0rP+TXtyPSi8lijQo1rIYCHPs/Erf9z3kjwxsnPrcP1C52Xs/fVZztLjti3PRkS0psQAv/bXIwLXk/BQQdPIf+yfA9+3WtbAjWhb//d7rW1+Q73T0P3uVDvw0OCPzvYZ5UqaNK/R3YAjAeQIoTIBrAbXXMQ/qT0StoNZtz4rv/28WD36Y4SvPBjrtphBNwX6WU4b67zoS02m8TMF9bjiW9zAhgVuWJ/Uu+owqL9nNth9L3NqfUb7kDJr2kPy3PDQIQQiQBOB3DEJGvO5s111QPKk/3NbLUN2Jvq28yK/2fvvuPkKsv+j3/v7G4a6ZX0QgqEEsoCIihFEBSwICj66M9H8EFUsIFKESmigIAgHaRLD0WQJCSkkV42Cel9s0k2bftmS7bO/ftjS2Z3p8+ZOVM+79eLF5vZ2XPuM3NmzjnXue7r0kw/57JS043yaK3JL9eMDdF3rS2qjPAc2c+Ltmxnsf9ag+1+UVhRq798vDGqaalO8dc1tmWK5tLc4MkBK/JKQs7qdPsr7lBNveMz08oP1+sfEQQjW0RbEs2fYDWUf/7aSp/v/+LtRXplcV5U6275mPj6TPx+yhotCFL6am1+mX77dvJkm7eIpESTpKgO/rFq6uVqgPCal1foGwEuoEKdLuuJY8Q9nm5803ckem/Z4ah3iIdmNn2ZhTPXv6HRoz9/uL5Nt+aWIOZjQbrhJLofPL9M17+2KubT9+oaPZq+PrqTm1W7SzX6lqltpo20t2Bbkd+goxvK2hV/jfXr/OcPN+jpeTvaPLZ8Z4hZkD4sbf685fupFVJaXa/JdzvbWdmf1gNvu8fbb2974dS2XLW7VAVedxZ9ZcmOu326rvt328DhqgjqNTV41XSNRzKhtXattfYUa+1J1toTrLX3xGI9C7cVxa2xTmII782786MNeinBan3+8o1VOvNvszo87vHYqIqVe7v1/XVtjqHttbyKyXb3HHBSXYQZtO+tzA96LHRKPK88jDE9JL0n6TfW2g4nf+3r5uaXBJ8+5++m4fq9HY9b33l6sY6945OAy7vJa/r41HWxq9e138+MhrLDdVoYZGqht0DBzHBtPeg/GONrP3lh4c4OTTnCjRE4EWz77rPR3/y/6pkl+lOQsj0tvLex/fleqOd/3g0+w/XjF5eH9LxwSjMFqwvpb39tsTlGU0UvePgzSU0BzBveWNWhCYd3B/BKrzqGP3h+me78KLwbNO333dZkJR97/5SV+frRC4Hfh0gSGj6J8to61n4Ro3Igsarr62qAcM7mgqjbe0uRZwY5JRHrC4Q6pfj9VaFfhKzIK9WrS3bp5neci+qHctEVrICr9+u/JNeZTEZjTFT1HNfllzveAfPZz3ZozZ6y1runwbp15+yKLCDmRB3L9mMLNTi3P4xivUWVtQGn9obTPTiSLa6oqW8z7TjcbsUPzdiiHSHsI0XtauW0HIdrIzgotLmZEmCjr3hqccApOC3aB6GvcL9eExwS6bdASw3DbQcr4jr13glT1+7XwUNHPm8tr8FT87brvIfm+a3J5EsinhfE2rr8cl3y6Hzf5SOAOLhpyho98ElsG6HFO9PTGJOlpuDg69ba90P5m6l+mtd4fy/5axzhPbujZSZGuNdqi7Y7m9USyvfpWffN0Q9fCL2D7bsrU/8mzP4y/8GpQAHSQEEtf91do52RFqoVeZE3DgqlxEG4gnW7/dLf5yr73o43HuPlhYU79fHa/QHLYDz86VYVVdbq9wlSIzaSmXO+ZjqlslifYibEFONAnp63Q7uD1NrydTc+VU7N95e33bZQT0ye8nMHdXdxdZt05qfm7Qg5mNgSOHKyEcp5D83TzA0HAm6Xd0yjfTv6qtqGNtu6LcBdvPYCTSkO5+LO13Mvf2Jh690bp9w3fXPYBYrjLdrz5oIwplxk3ztL5zwwN8o1+rZ+b7lKm1vIP/vZjtaA51KvzN0rn16iL/09loeRjwAAIABJREFU8vU/MXe7VrWcrAR44X7tp6ZFVQR32EL5/miZWlLqddLjr8BzOKrrGvTwzC1xO4lEZJy69r3okfl+P5+JHjhs/xq0XJDc8eH6tAz8heq+6Zu0+UCFPo+yoUxtQ6MemrElbh1vn5obn4yzRJeKjaxq6ht1wUPztGRHbKZhxZppuuPygqRN1tp/RLu8cL+9fvB86AG3WEqU84aIjo8hvuhOdqNtacDwSYTTsXcF6HD6/X/5LnHlcfnY6ESZB38CNW+7/YPg5UDa3+hPNA2NHmXfO0tTfATN21/zx7KnQQt/DTzvm7apTbOjUPa4eJV9ikYijjChA4QllXV64JPNuuPDwKmuP/t3+FHj91fla9xt01Rd15DQwcSz7guvDXcL72l7LX70wjJ9+cG5enRW21oJS3eGd+Lk9DEgnPTq+6cfuTNc1+DR8XfOCNgJOJDrXwsv3Tec7JFYevazXEeW89S87Y4sJxW0/3Iura5vvQO+wWsqt/e0bl9Bs037Dzl+kVVU0fYuf24UUx3nbSk8MnXYzxEp3IoNoTaweWz2dj0+Z7veztnT5nFfwYREDMLsLTvsswZlvPl7ZYora2PaTMZJ5z04z+0hRGT5zhKf2dCVtQ3a3e6CynsXXrS9SP+a78z3thMKKmoiqnE4a+NBXfrYAr2wcGdMP6OvLt6lJ+Zu73Cse2VxXpubNE6JVQ2fWIlV4NTtr91YrH97QaVyi6r0l483Bn9yYjpb0o8kXWCM+bz5v6/Ha+XR1smrqW/02+AtHLFophAvR5IrAjvs0Oc6+95PNXtzQfAnxsnoW440b4llIGTWpsinjN8/fbNW7S7VoQiCtOEkNsTbQzOcvSm/p6Ra+8rD7/77yfoDYc1Y9OfZ+bl6L8zlxDvjO5Jzo39GUKbNyWQtXxI6QNj+QrWsuj7oXP8Wwd6f372zRg0eG1FwscO6wnque2dgLW2/259gPzkntEBR+0KqT87d7li3okj4qlGzeneZVrabWvun/4R/IeSxTd1fvSV7J+v2/v5J8MBqeZDU+WQWi4usr/1zge76r7MXIjvbZVCHW6/E+7vwJy+v0Bl/mx1SQ55QhdrApuX1rm93suL9HZLIRfrPvn+OLnok+LTreNmwr7xNcPW0e2fpC/fNdnFEoYtnp75IBBqdr7Ff+fRifflB/9nE//P8Mv112iYHRuYM7zpd3nfXCypqdPnjC/0Gmn/x+ipt2HdIf/l4o+ZtCVziIlJ1DR49O78po6+use139J0fbdDVfhq0OWHh9iJd+A9nM/9D4euCYvOBQ34DK8FqwSF1WGsXWmtNc83ck5v/mxbob7xnVYXaPCJWqusao5ppkQqcDny/sDBwx/SIG7BEIJLrq0TS0iA1r7haVzy1WC9H2ZzDWyKc5jwxd7teXxbZd8DoW6aqsKK2zTnCl/4+V394d23Yy7r+tZX6nUPlyRoaE+CFDSCS6dGJKOoAoTFmhDFmrjFmozFmgzHm104MzJd1e8v11Qgv0Pyd8OYWVvm8oxGrC9WtUWahhXqgaUkvD0VOiDtz+6KiD87Y0mYaojdrrQ7VxC649PqyXT7rFy7JLdZ3nm4bsHhtqXtBzFDtKanWXQGKwi52qUv0w2F25gqUhh/JnblY8r7ICvULfUEYBbBjJdSvpsufWOi3Q931r61KyJR2tzNYnOTUppRV+y68fuljC/Utl0sOHK5r1H9W7w3+RAe4sW+E+hnxFbSPdLhuZs++tXyP1u0tD+mCoqa+UXM2H1SpnzpmkfrXgtyYXeAWV9bq759sDtg10t93Zjx4X4hNW3dAX/r7XD2/IHEyTwNZvL1IN7yxyu/+2/JwoAY9cIZ3bcH2NzSS9RibKOOO5trQqXOuB6bHtsZmOJLh+ioaiXzTOlQtU3Ij+Qyt9tF4MNwA2IZ9idWs7z+r98a0g7hTmcDBJEMNwgZJN1lrJ0n6gqRfGmMmObDcsASrzRboQurjtb6yaWLzrfBKGJ2RohHo7n6g7I31e8tV3+jRy4t2ap2fosSh7JNPzduhk+6a2aYTqhMW7yjWruIq3f7B+phcHLuV4Tl7c0HAO1eR1IGJdCqyUXQNWhJJqLUnwg1eOt2AJhasTczu4sY0NTNoKXWQKCf+iaJ94fifvpITVuF1p4Tyvtzz8Qb95u3PtSyGUzTdPEF3c9cMdLPFl3hPLz9UU69rXs7Rta+scHy5LZ6cu0M7HeoeLTXVkHxq3g7ND9LgK5HcOzVxMk8D+eELy/Tx2v0hZc2EM+W00WN10ztrQp49BGekQmAkUTh9HKlr9KisOn5ZgrGSDPtYUWWd/uf5pQlZ9iZUbtfgu/SxhcGfFIZo34rfvB3b2YBONN9NBFEHCK21+621q5p/rpC0SdKwaJcbrjXt6li1D3AEql/x0Zp97f42PMn2vRGoi9Nljy/UA9M3667/btTlT0T+oZ7ePIXRuyOkU37yUtMFSaJPU5OaaiZ5C9RRLFFYWZ93BSvpTCmp6eLG6QY0obLWavOBjl3bd/lp5OSxNiFOwq55eUWbAPjlTyzUo7Oagpe+PsVvrtij0bdMTcmi+cHc+Gbb2qjb4p3RFMb+0hKUqgrwPrlZpqCh0aMn524Pu6h2+5cgkiNNvC8o4j29vL55mk9egGL2Tvi5g50Ja+ubMikCZRD6kio3zNzkfRwK59xtW0GF3luVrxvfONKsq30ZmXDXn+7Yn+Mr5ENBGG9LLEsthMpfF+xoJVowzumO3OlkT4nzGeOJ/v0VSl+Em6es6dAPItE4WoPQGDNa0imSOqQ7GGOuM8bkGGNyCguT5+5tKN5wsQ5fi5cW5Tm2rLV7g0S/Hf5shnveVhtFwdVg054XR9Dprn2A2VtL98sW5z00L+zlx5t3Iw5vJ9w5I84jSUxuFiR+f9Ven8FbfydqiXKeNSdAwWxfJ4NPzW2qi1ocx1o68RIsq7p9U5r2Ijl5Lq+ub9P5zQlLc4tDmkox+Z6Zjq43HO+tyteDM7bosTmJl0mbSqy1jmS0bNhX3iHzffOBCq3ZU9am0Lq1Vrd9sE4rvBok1dQ36vkFuWEH/8KRCEGmWG5fsvjO00scn50SiUQ5vsZavLqJB/Lh5/EpZRFMJF8BLQENJ8suhVuLOhZidS7se1afu8LN6AcCeXdlfmuSRKRiffhxLEBojOkh6T1Jv7HWdogwWGufs9ZmW2uzBw4c6NRqE4ITJ2wHymt8dkcMVUt2TkFFjZ6etyO6wbhw0hOLuwy+/PSVnIC/XxVBcdHbP1gf6XCiUlRZG5M7bTM3Rt4JzK1D6JzNkY85mfjqnByIx9qEu4i5O4QmLuWHU7c5jq/mSrE2+Z6ZusmhAtGStLu4Wlc/t1RLcyM/ZoWr7HDHANSTc7dr1ib/weeWzMFqF7Kfg33sgk2dTaZA0NOf7dDJ93wa9TTnx2f7bpj2zScXtelC67HSG8t263vPHqk3fOwdn+jeqZsC1sQM5xWtrG1IiMBIe8fcFrA/RcoINi3ujL+515ApmWMFh+sbQ8pK3+WVGZwITXEOxGA2UiSi+Vb+5hPOlkVK5v0wkKLK6N7rlkNnNMkkyWbauuBB1UD7brzONnLyoj9nDLdJScvMAYTHkQChMSZLTcHB162174fyN/6mxDkl0S6KfTHGKLf5ImFv2WF999nQuoEGUlPv0QOfRFfAdnmQD/DyvBKfDUK8rd/rOwvNn1gHBGobmk70NwTLjkwiLyzcqVfjVNMyVLEs/BrINS93DPy+tyrf9VT0h0JINY8lTwJ8Eb67Mj/g78t8TEGtDnNKaDIqrqxtkwEVax+t2ac5mw+GHvTws+ts2Feu+ds6zgJo2dVmbTyouVv8B+3C1ZJF6uuY8uCMLfrVm6s7PJ4MzveTTe79slfEsNGXkz5tvqm0r7zpRl8svnbWh1jovLquIWi2bCgX1ifcOUNff2xBSOuMlvvf0s657tWcoN8x8YxrxPIQWFJdp5+8tNzxRj2xFqjMUKJKlGmnkbx2LTeqqoKc1yTC+Vo0Qrn5652J135znTxfd3rGRKL7xeur/P4uXk0zQnHlM9HHOcL9nDyS4FN5I5boTUpM06f9BUmbrLX/COVv9pfX6NwH50W76qRnrY2oXXgiuDSKE+fT/zrLwZGEZuKfPtEHq/ODHqCTzSKXuhv7c9MU57KUopUIWWhPzPWdEdNeoAumuijuggZKQorX3ec/f+g/w7aipl7FSXZxFapg5zBXPbNEV4VwsuTk+3TNyzm6d2rgk/hgq7v0sYX603/avqfXvJyj2c3TyN/O2dNaJ9bb+ghvzkSz/0frW08u0qtL8iKbVubAyduJd82MWZ2naPnaL+NdDN3XS/zE3O0af/t0R4KruYWxvZHdQZxePmutnl+QG5N9a/bmgrg1gVm1u1SXP74wpJsewb5HF20v0qwwZ0+8sjhPc7cU6pUleWH9HdLLvVM3hZQR7uQUZDe8GUK5rWizA0M1/vbpcVlPqgj09RhK4Lb9+U4sry9Km5MKaus9aRcIjicnMgjPlvQjSRcYYz5v/u/rgf4g1C+IeFzApmMRfCeEGmjz9cVSEWbHWKf89u3gwSsnLuri3cyjvtGj6SGklyebxduL9I9PE+/OT7QZur4si6K8gOT/RpInwElpItysduu7IBHkOtid1Vuw7L1dUTSViCaLY+P+8LLKIxHK9JXDdY1aGaSUxA6vwNAby/xf9Gw9WKGP17atQdty3tLosY4EYEqqEmNqXbhqGxpjNk060KlhS2M0X5nJkVi4rUj7m6dO1zZ4dPb9cxzNkI2XK55a1DpL5fM9Zbp36ib9vt0NvZZ3q+Wia9vBiqhK37RZdpi7QnFVrb7xxELtK/NffuaujzZo3d5ybQmhHluwWS3/8/wy/fTVwCVogEh9vid4+aJwPyPxKs3kpAXbEiupAU1i3WwsFlbuLtWHn/vvAYDoONHFeKG11lhrT7LWntz8n+tFUnx9z17zcseshmQrPBqvO7NOqaqN/s5usrn1/XVxXd/jc7br5wHSy2Ph30tjP7X5AZen5/rj1AWTN1/TcEur6jT6lqlRLTdQKn4CxAcRgWAXEWv2xG762Gtx+NxHI5TpK394b62+8/TigM8J9SbPVx+Zrxve8D3F+e7/btCpf/lUVWHcMIpX3UHvY+6q3UcuXINeoHr9XctzrbU+p91N/NMnuvaVjudcbookwP3DF5bp/VVNdQ33lh3W3rLD+slLKzR7U3LVvV21u6z12NXSgdpXxtKCbYUaf/t0rdpdqoseme9I6Zv2/J3zeb87S3NLtDa/XC8uzPP7N2vzm7KS31+Vr4KK4PUvY13aCPAnFjdk/5MgjVuQuqwimxmweEfsA8G5IdRSTWWxLqHlaBfjROerk2aiTt/xJ1ZZJ7Hyj08TM8jjT5UDGaUH49hdzxgptzC9vyQDibZLlJvC6VL37Ge+GxOVVNUlTO0eJIdAe0ugbtTx8PHafVqbH10ANJI6tJF8gt5asUdS6PU0P1qzT8fcNi3uQQxfDQvCuRwo9crUax/EmbclvBua4Z7wWtuU7faMn+8/f4LdlHwkhMz1a4M0PEsEkcyQacnwWRHkRlhDo0efrN/fenzxfufeXrFHu4qrZK1VTl5JzI9BryzZpWt91CFuGteRdUcagOcY6rxNccgoTzaLdxSH9fxkamQVipZA1O4kzGZLCnH8Hvvje7FPlEmtvT/xJHSAMNWPyeF24klGK/LC7wrsppcW5UW9jHg3Qvl4bepNL05n65qzIkKp59Jixgbf2Sxr8sv9lgNIhMTdYBfq2wrC69qcLMJ5bxPhfYqHzQf8XDB6vQA3vLFa33C4E6TTIj1vmdb8Pf7sZ7kOjiZ5vNMcUA3XKfd8qvunO1v24Z+zfd9YCtacLRx/nbpR934cvKh/NP41f2fMlv3s/Fxd/9oqfbL+QIffzd5coG89uUifrD+gK59Z0hosj6VQMggjxbRI533tn/FpAITkYWVV29CYdIkwqayipkFrorwpm8we83MukAislf7w7pqwb8SGKqEDhNE0GYj2jl88pr0mw92f94J0IHVCuE0K9gaoSZMIUq0RCuLr8icWSnKuo53b00IDZVC9sCDwBWx+aWJ/1gMJlA0VzgVzJGUwnpy7PeLp6W4FJC951JkLRl/H/leW7FJZdXLMFpji55hbWdvQ2g0zFdQ3epRfeiRT5PUwgube/E0HX72nzPFGVasc7P76rwU79fzC5u8/B08FK2sbtKC5y3iDp2MBd6ey4R5sLgFS1Hz+1v57o7S6XrtLmt7fne0u+MOZshbraVShSISGZ+lubxKfC0ipV0opVpyqHYuOIrk2vXnKmg5N6dJJItbBb9HgsXonJ1+vLonNNV5CBwijac1d62Lnw1QSj660TmTtpSsaOKWm617N8ZkZ+tz88LOLGhL4RkTrBbIfG4IUlodvD4ZQv/OBTzaHFUSsqKnX3BjdqXTS836Czn+btinm635whvMNjFqccOcMnXDXDBVWdGxa0hIQclKw7oC+uxiH7o7/rNc5D8wN+fneca1QvtN+9eZq/e9Ly9suI+S1ucOJIMLv3v5cP3phud/mHo/P2R79SiIQ6bZFG8+M5Uwkph7HT0Wcm/857TtPO1/LEwhHLKf1f+ZCfwS+f2MroQOE0YhVyqWjuKOUcuL9JTkryQqlhyWNv/xnbnTufa2J4kaL297Oif3UtFTTvu6mv5MoX8HmhduKdMhPZ+m7/xub6ZArd5XoX2EGvqvrGrRxn++TXX8diuvjUNLjybnh1cILV6PH6vS/zurw+I9eaBsICzfj6Y1lu1XndVN15saDGn/79JheUPg6Rws1syzU7Wsp1xDpeuLByRkRe0qqtbq5SZG/G+xT1+4P6cIq1E/LzsKqDp28U13L/tP+NfrvmvR6HYBoFFe1vdlV2+Dxe1xH9GJ5SRWPMhLtMVsvtjLdHkCs0K0MbmC/c04iZ74lkwo/AR+mvCSn4qo6DejRJSbL9u6K2H5q36EYTbMLlFmx+cAhn5lQN76xWrP9NEwpqqzV8p0lOmNMP0fGt/lAhYoqk2N6covJd89U3v2X+vxdZW2Dfvt225kBt32wTgUVNa2Bj5YGSevyy3XckF6xHayXUKeTJsK0U6ecff8cx5b1pb+HnpEp+a4RHe5h4cVFO/XiIqlTgD98bn6uPvYKnvk79vgK/CbLfcK1+WW68U3fHc0BdPTh520D6mXV9frJyytcGk1gLwaZ7ZIMfHWtB/xJ2QBhrOZkOymV6gkBTvMX2ALSSftr6W89GXqTDmut/h1GDUq/jUJc4q8uob/goCTl7CrVd59d4jdAFomiyo7TehPF+jCaYm3af8hvbdPSqjr1Oyr0wPPtH3SsS9RSLzOVAniJorCiVqt2J1fTN2/7yoM3EVmzx3+NxwIfU+vd5v3d7K8WJoDk98Rcd0ozOGkD2ZkIQ8oGCKMRry60d360IS7rQfwEasiA8LQUOAcSycFDNVqwrUhXnjY8or9fvbtM2w/6787s5H4/Y8MB/fnD0I8z66n56LjobwSagPUAL3t8YUhLWbKjWN//11J9L3tElOPxL15JyV8LsaFNg8eqpr5RXbMyJCVPNpovP3x+mbYE+N4IRySNjyLhxMvdMtSSMJvZOTkGAADSScrWIIxGXjGBCUTm/umxK1IP39Kt/hHc9b8vrdDNU9bon7O2afQtU9XgJ3jjLxhx45urA9ZOOVzfqOo6Z7JR/vDu2g6P+Zsue4aP2nZu8dWII1l9/19LQ3peoKy7GRsORD2O3SVN5S82+qkr+MmGA1qRVxJwGW5WJfjcK7usOIxg0RO+mnI4tCGjb5mqt5bHp/aSUzcOrKw27vN9EzxYbcJGj9VPX8mJayZjqEHdUJ7nRGD0j++u1dR1HRuIEYkEAKQKAoR+5BVRSw5IBje8Qd2fSOT4qD+F4FqCV4/M2ipJqmmIrpX46FumauG2ojaP/XP2kUYj0VzT+mo44q/xhL8pfKNvmRrT7ry++GrEESvfenKR8ktjd1PQiSYUjXGox3rwUK0Wbi8K/sQQJFKAN9ZTPw8cCj51tj1/gbjFDr3+UtM+U3DI9/twl1fDoTG3tu1kPm19U/DL1xD3lR3WrE0H9SsftfZ8bZET+22iTVf31Thr9e4y7Q9hCjUAAMmAAKEf176S4/YQACBmaAITO0t2FGvxjuKQn//MZ22739bWewcd3cnb8g4QRNudd+O+Q/pg1d7gT3TB53vKdM4Dc1XbEP/yEKHUNvz1W87cAKltDmTHNGDWvKv+7p01HX61Zk+Zxt02TQUV/gMpsZgCvKOw0vmFRumN5bt9Pn79a6scW8dPX8kJqQu892t+4l0ztaekKaD9wCdHbgqMvmWqthwIf3rzgzO2BH3OG8t8vxbJ5LOthbppSsd9HgCAZEQNQgAAHBTqtFI3xTPs+PXHQqsZ583f1O1IBdvelxblObq+UISSYbVh3yHNDdCUJVQttSh3xnl2xIq8EuUWVmrxjmI1eKwWby/Wt04ZFrfMMO+mLIlySyS/NLys0jmbD4a9Dqdr+M7bUqCvnzjE7++NQn99W7KidxdX67YP1gV8brRBY+/uyQAAIDgChAAAhCiRO9qGKq+oKqQM0k83hh+YcMpXH5kf1/XV1Cdug6n/fJ68QY6rnlkiSfrmyUNdWX99Q8f9PB7B8em+6tRJenjmFs1YH15NyWtezlG35kYriSqShPQ6h28CtOfxWD2/cGfMlp8oAWcAAJzEFGMAACIUrLB/JF5enKe3/ExDdMJLi0K7aN5ecGR65vq9vhsbxEquA5luJdX1bf59qKbekeU66Y1lu/Xz11a6PQyfpqzco9G3TFV5u9cxVmLRXHe5n8Yrh+sam5uMxOZz5q+Rx+Nztju+D67eXarRt0wN/kRJWw8mzpTrKStDmAIdxfJjHsAjQggASEEECAEASDC3vN809S4WGYvztwVvhtD+2ndfEhbhr2vXQGaNVyfcRHHbB+s0PcyMMk8I6VreU7QjDWKvaG5klF8W2nRVN7scB/LwzC2afPfMNq9Dy+eq5XPmtFjUU/Tn208tjt/KHNDSTfjTDc5lKBdU1KqsOnB36/Mfmqd7Pt4Y8DlhSdQdHgCAKBAgBACkDWPMCGPMXGPMRmPMBmPMr90fU3zXF+86dIngUE18suDiYext04I+Z9zt03XtyyskxacLcrTW7CnTDAcDRt4en7Nd5YePvP8mDh84p1/xwy5PgT/opyNyIvHu/i5J87cWujQSAACSFwFCAEA6aZB0k7V2kqQvSPqlMWaSy2NCjM3aFH2jj1gKN+MslK7Ls5ubm4TTUdv3ujxt/h+JluzNV5fk6c4P17satAwlAzNaL8Sw9p0bXly00/GsyFAWt3yn7ynivph2KX0/aQ6QAwCA0BEgBACkDWvtfmvtquafKyRtkjTM3TFF/rclVYGn1aWrFxbu1DOf7WjzWPsAQjL79Zufh/zcaBuw3P3RhpCCaoEy8/KKm6Ypr9pdpleW7FJRpXv77a/eXO3aulNBuF2Y20uVT2GqbAcAAN4IEAIA0pIxZrSkUyQta/f4dcaYHGNMTmFh4GlqscxF8p4W6c+pf/k0JuveWVQV98YkTvqLk7XGHNI+G+qdFUeaNISbUTdzY3h1C6Oxs6hK/10bvJNyKPtrIqiobYjp8gsqOtbrtNYmxVTveIjVq3Dg0JHA5cVx7oIOAECqyHR7AAAAxJsxpoek9yT9xlp7yPt31trnJD0nSdnZ2TG/qveXePXdZ5bEetV+fefp5Gp8kAy++2zb9/PhT7e2/twyHdhpHo/VP7zWE4lDNQ16fWnsumrHy4IQmvM4YcO+Qx0eG3Nr8LqR6eKZeTt0wbGDgjYVCde0dUeC5lsOVji6bAAA0gUZhACAtGKMyVJTcPB1a+37bo9nR0Glz8dDvciNdgppuqj3RF5DL1kt21mizQeiD5Yszwu9Flw6emrudreHkDT+vXSXfvLyCpVWJ2bG6dr8Mo2+ZWrrvx/5dKs+XrtPu4rbNnfaUZh+zZ4AAKmPDEIAQNowTYXSXpC0yVr7j2iX5y+4F466xuiSFI+945Oox5AOfvHaKreHEHcepztLwKcGj5W1Vm+t2KMpOXuC/wESUkFFjabk5Ld5LLeoSje8Qd1KAEB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\n",
      "text/plain": [
       "<Figure size 1296x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfms  = [None, [Categorize()]]\n",
    "dsets = TSDatasets(X, y, tfms=tfms, splits=splits)\n",
    "dls   = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=128, batch_tfms=[TSStandardize(by_var=True)])# per feature normalization\n",
    "dls.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lin_zero_init(layer):\n",
    "    if isinstance(layer, nn.Linear):\n",
    "        nn.init.constant_(layer.weight.data, 0.)\n",
    "        if layer.bias is not None: nn.init.constant_(layer.bias.data, 0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>1.408996</td>\n",
       "      <td>0.189189</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.386957</td>\n",
       "      <td>1.416851</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.387878</td>\n",
       "      <td>1.406260</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.387230</td>\n",
       "      <td>1.392632</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.386742</td>\n",
       "      <td>1.364293</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.386459</td>\n",
       "      <td>1.352235</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.387222</td>\n",
       "      <td>1.377085</td>\n",
       "      <td>0.310811</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.384431</td>\n",
       "      <td>1.409551</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.382965</td>\n",
       "      <td>1.383275</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.379280</td>\n",
       "      <td>1.375444</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.376708</td>\n",
       "      <td>1.369349</td>\n",
       "      <td>0.229730</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.373009</td>\n",
       "      <td>1.424380</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.369465</td>\n",
       "      <td>1.347130</td>\n",
       "      <td>0.445946</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.364685</td>\n",
       "      <td>1.349344</td>\n",
       "      <td>0.351351</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.359562</td>\n",
       "      <td>1.355861</td>\n",
       "      <td>0.337838</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.353657</td>\n",
       "      <td>1.368653</td>\n",
       "      <td>0.283784</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.347755</td>\n",
       "      <td>1.368211</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.341900</td>\n",
       "      <td>1.388963</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.335846</td>\n",
       "      <td>1.323503</td>\n",
       "      <td>0.432432</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.329689</td>\n",
       "      <td>1.348774</td>\n",
       "      <td>0.283784</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.325552</td>\n",
       "      <td>1.420805</td>\n",
       "      <td>0.229730</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.319648</td>\n",
       "      <td>1.414717</td>\n",
       "      <td>0.229730</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.314585</td>\n",
       "      <td>1.324103</td>\n",
       "      <td>0.445946</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.308463</td>\n",
       "      <td>1.329270</td>\n",
       "      <td>0.391892</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.302946</td>\n",
       "      <td>1.367105</td>\n",
       "      <td>0.310811</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.296695</td>\n",
       "      <td>1.403357</td>\n",
       "      <td>0.270270</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.290560</td>\n",
       "      <td>1.389852</td>\n",
       "      <td>0.243243</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.284906</td>\n",
       "      <td>1.335073</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.278813</td>\n",
       "      <td>1.302893</td>\n",
       "      <td>0.459459</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.273061</td>\n",
       "      <td>1.313337</td>\n",
       "      <td>0.486486</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.267115</td>\n",
       "      <td>1.342506</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.261160</td>\n",
       "      <td>1.366756</td>\n",
       "      <td>0.310811</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.255165</td>\n",
       "      <td>1.387934</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.249634</td>\n",
       "      <td>1.376279</td>\n",
       "      <td>0.310811</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.244186</td>\n",
       "      <td>1.353527</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.238805</td>\n",
       "      <td>1.329386</td>\n",
       "      <td>0.391892</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.233417</td>\n",
       "      <td>1.314354</td>\n",
       "      <td>0.486486</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.228009</td>\n",
       "      <td>1.304554</td>\n",
       "      <td>0.459459</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.222676</td>\n",
       "      <td>1.300016</td>\n",
       "      <td>0.472973</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.217936</td>\n",
       "      <td>1.302480</td>\n",
       "      <td>0.472973</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.213271</td>\n",
       "      <td>1.308691</td>\n",
       "      <td>0.486486</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.208589</td>\n",
       "      <td>1.316804</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.204154</td>\n",
       "      <td>1.324514</td>\n",
       "      <td>0.364865</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.199818</td>\n",
       "      <td>1.331150</td>\n",
       "      <td>0.391892</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.195905</td>\n",
       "      <td>1.335782</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.191937</td>\n",
       "      <td>1.338519</td>\n",
       "      <td>0.351351</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.187629</td>\n",
       "      <td>1.340027</td>\n",
       "      <td>0.337838</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.183921</td>\n",
       "      <td>1.340437</td>\n",
       "      <td>0.337838</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.180455</td>\n",
       "      <td>1.340518</td>\n",
       "      <td>0.337838</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.177170</td>\n",
       "      <td>1.340513</td>\n",
       "      <td>0.337838</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = create_mlp_head(dls.vars, dls.c, dls.len)\n",
    "model.apply(lin_zero_init)\n",
    "learn = Learner(dls, model, metrics=accuracy, cbs=ShowGraph())\n",
    "learn.fit_one_cycle(50, lr_max=1e-4)\n",
    "learn.plot_metrics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43243243243243246"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eps = 1e-6\n",
    "\n",
    "# normalize 'per feature'\n",
    "f_mean = X_train.mean(axis=0, keepdims=True)\n",
    "f_std = X_train.std(axis=0, keepdims=True) + eps\n",
    "X_train_norm = (X_train - f_mean) / f_std\n",
    "X_valid_norm = (X_valid - f_mean) / f_std\n",
    "\n",
    "import xgboost as xgb\n",
    "classifier = xgb.XGBClassifier(max_depth=3,\n",
    "                               learning_rate=0.1,\n",
    "                               n_estimators=100,\n",
    "                               verbosity=1,\n",
    "                               objective='binary:logistic',\n",
    "                               booster='gbtree',\n",
    "                               tree_method='auto',\n",
    "                               n_jobs=-1,\n",
    "                               gpu_id=default_device().index,\n",
    "                               gamma=0,\n",
    "                               min_child_weight=1,\n",
    "                               max_delta_step=0,\n",
    "                               subsample=.5,\n",
    "                               colsample_bytree=1,\n",
    "                               colsample_bylevel=1,\n",
    "                               colsample_bynode=1,\n",
    "                               reg_alpha=0,\n",
    "                               reg_lambda=1,\n",
    "                               scale_pos_weight=1,\n",
    "                               base_score=0.5,\n",
    "                               random_state=0,\n",
    "                               missing=None)\n",
    "\n",
    "classifier.fit(X_train_norm, y_train)\n",
    "preds = classifier.predict(X_valid_norm)\n",
    "(preds == y_valid).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROCKET is a great method for TSC that has established a new level of performance both in terms of accuracy and time. It does it by successfully applying an approach quite different from the traditional DL approaches. The method uses 10k random kernels to generate features that are then classified by linear classifiers (although you may use a classifier of your choice).\n",
    "The original method has 2 limitations (lack of multivariate and lack of GPU support) that are overcome by the Pytorch implementation shared in this notebook.\n",
    "\n",
    "So this is all the code you need to train a state-of-the-art model using rocket and GPU in `tsai`:\n",
    "\n",
    "```python\n",
    "X, y, splits = get_UCR_data('HandMovementDirection', return_split=False)\n",
    "tfms  = [None, [Categorize()]]\n",
    "batch_tfms = [TSStandardize(by_sample=True)]\n",
    "dsets = TSDatasets(X, y, tfms=tfms, splits=splits)\n",
    "dls = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=64, drop_last=False, shuffle_train=False, batch_tfms=[TSStandardize(by_sample=True)])\n",
    "model = create_model(ROCKET, dls=dls)\n",
    "X_train, y_train = create_rocket_features(dls.train, model)\n",
    "X_valid, y_valid = create_rocket_features(dls.valid, model)\n",
    "ridge = RidgeClassifierCV(alphas=np.logspace(-8, 8, 17), normalize=True)\n",
    "ridge.fit(X_train, y_train)\n",
    "print(f'alpha: {ridge.alpha_:.2E}  train: {ridge.score(X_train, y_train):.5f}  valid: {ridge.score(X_valid, y_valid):.5f}')\n",
    "```"
   ]
  }
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